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Update parquet files
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- .gitattributes +2 -0
- README.md +0 -104
- binding_affinity.py +0 -147
- bindingdb.ipynb +0 -1258
- bindingdb_single.ipynb +0 -1036
- biolip.ipynb +0 -586
- biolip.py +0 -41
- biolip.slurm +0 -10
- combine_dbs.ipynb +0 -1763
- combine_predictions.py +0 -10
- data/.gitattributes +0 -1
- data/all_ic50.parquet +0 -3
- data/all_nokras.parquet +0 -3
- data/bindingdb.parquet +0 -3
- data/biolip.parquet +0 -3
- data/biolip_complex.parquet +0 -3
- data/cov.parquet +0 -3
- data/dcoid.parquet +0 -3
- data/dcoid_predict.parquet +0 -3
- data/dcoid_rf2.parquet +0 -3
- data/deepcoy_dekois_predict.parquet +0 -3
- data/deepcoy_dude_predict.parquet +0 -3
- data/dude/_common_metadata +0 -3
- data/dude/_metadata +0 -3
- data/dude/part.0.parquet +0 -3
- data/dude/part.1.parquet +0 -3
- data/dude/part.10.parquet +0 -3
- data/dude/part.100.parquet +0 -3
- data/dude/part.101.parquet +0 -3
- data/dude/part.102.parquet +0 -3
- data/dude/part.103.parquet +0 -3
- data/dude/part.104.parquet +0 -3
- data/dude/part.105.parquet +0 -3
- data/dude/part.106.parquet +0 -3
- data/dude/part.107.parquet +0 -3
- data/dude/part.108.parquet +0 -3
- data/dude/part.109.parquet +0 -3
- data/dude/part.11.parquet +0 -3
- data/dude/part.110.parquet +0 -3
- data/dude/part.111.parquet +0 -3
- data/dude/part.112.parquet +0 -3
- data/dude/part.113.parquet +0 -3
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- data/dude/part.115.parquet +0 -3
- data/dude/part.116.parquet +0 -3
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- data/dude/part.118.parquet +0 -3
- data/dude/part.119.parquet +0 -3
- data/dude/part.12.parquet +0 -3
- data/dude/part.120.parquet +0 -3
.gitattributes
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@@ -839,3 +839,5 @@ data/dcoid_decoys.parquet filter=lfs diff=lfs merge=lfs -text
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data/moad_complex.parquet filter=lfs diff=lfs merge=lfs -text
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data/pdbbind_complex.parquet filter=lfs diff=lfs merge=lfs -text
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data/all_maccs.parquet filter=lfs diff=lfs merge=lfs -text
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data/moad_complex.parquet filter=lfs diff=lfs merge=lfs -text
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data/pdbbind_complex.parquet filter=lfs diff=lfs merge=lfs -text
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data/all_maccs.parquet filter=lfs diff=lfs merge=lfs -text
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+
default/binding_affinity-train.parquet filter=lfs diff=lfs merge=lfs -text
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default/binding_affinity-no_kras.parquet filter=lfs diff=lfs merge=lfs -text
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README.md
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---
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tags:
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- molecules
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- chemistry
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- SMILES
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---
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## How to use the data sets
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This dataset contains 1.9M unique pairs of protein sequences and ligand SMILES with experimentally determined
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binding affinities. It can be used for fine-tuning a language model.
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The data comes from the following sources:
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- BindingDB
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- PDBbind-cn
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- BioLIP
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- BindingMOAD
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### Use the already preprocessed data
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Load a test/train split using
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```
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from datasets import load_dataset
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train = load_dataset("jglaser/binding_affinity",split='train[:90%]')
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validation = load_dataset("jglaser/binding_affinity",split='train[90%:]')
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```
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Optionally, datasets with certain protein sequences removed are available.
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These can be used to test the predictive power for specific proteins even when
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these are not part of the training data.
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- `train_no_kras` (no KRAS proteins)
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**Loading the data manually**
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The file `data/all.parquet` contains the preprocessed data. To extract it,
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you need download and install [git LFS support] https://git-lfs.github.com/].
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### Pre-process yourself
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To manually perform the preprocessing, download the data sets from
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1. BindingDB
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In `bindingdb`, download the database as tab separated values
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<https://bindingdb.org> > Download > BindingDB_All_2021m4.tsv.zip
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and extract the zip archive into `bindingdb/data`
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Run the steps in `bindingdb.ipynb`
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2. PDBBind-cn
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Register for an account at <https://www.pdbbind.org.cn/>, confirm the validation
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email, then login and download
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- the Index files (1)
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- the general protein-ligand complexes (2)
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- the refined protein-ligand complexes (3)
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Extract those files in `pdbbind/data`
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Run the script `pdbbind.py` in a compute job on an MPI-enabled cluster
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(e.g., `mpirun -n 64 pdbbind.py`).
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Perform the steps in the notebook `pdbbind.ipynb`
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3. BindingMOAD
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Go to <https://bindingmoad.org> and download the files `every.csv`
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(All of Binding MOAD, Binding Data) and the non-redundant biounits
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(`nr_bind.zip`). Place and extract those files into `binding_moad`.
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Run the script `moad.py` in a compute job on an MPI-enabled cluster
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(e.g., `mpirun -n 64 moad.py`).
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Perform the steps in the notebook `moad.ipynb`
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4. BioLIP
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Download from <https://zhanglab.ccmb.med.umich.edu/BioLiP/> the files
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- receptor1.tar.bz2 (Receptor1, Non-redudant set)
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- ligand_2013-03-6.tar.bz2 (Ligands)
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- BioLiP.tar.bz2 (Annotations)
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and extract them in `biolip/data`.
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The following steps are **optional**, they **do not** result in additional binding affinity data.
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Download the script
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- download_all_sets.pl
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from the Weekly update subpage.
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Update the 2013 database to its current state
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`perl download_all-sets.pl`
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Run the script `biolip.py` in a compute job on an MPI-enabled cluster
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(e.g., `mpirun -n 64 biolip.py`).
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Perform the steps in the notebook `biolip.ipynb`
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5. Final concatenation and filtering
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Run the steps in the notebook `combine_dbs.ipynb`
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binding_affinity.py
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# coding=utf-8
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# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""TODO: A dataset of protein sequences, ligand SMILES and binding affinities."""
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import huggingface_hub
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import os
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import pyarrow.parquet as pq
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import datasets
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# TODO: Add BibTeX citation
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# Find for instance the citation on arxiv or on the dataset repo/website
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_CITATION = """\
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@InProceedings{huggingface:dataset,
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title = {jglaser/binding_affinity},
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author={Jens Glaser, ORNL
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},
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year={2021}
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}
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"""
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# TODO: Add description of the dataset here
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# You can copy an official description
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_DESCRIPTION = """\
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A dataset to fine-tune language models on protein-ligand binding affinity prediction.
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"""
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# TODO: Add a link to an official homepage for the dataset here
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_HOMEPAGE = ""
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# TODO: Add the licence for the dataset here if you can find it
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_LICENSE = "BSD two-clause"
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# TODO: Add link to the official dataset URLs here
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# The HuggingFace dataset library don't host the datasets but only point to the original files
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# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
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_URL = "https://huggingface.co/datasets/jglaser/binding_affinity/resolve/main/"
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_data_dir = "data/"
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_file_names = {'default': _data_dir+'all.parquet',
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'no_kras': _data_dir+'all_nokras.parquet',
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'cov': _data_dir+'cov.parquet'}
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_URLs = {name: _URL+_file_names[name] for name in _file_names}
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# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
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class BindingAffinity(datasets.ArrowBasedBuilder):
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"""List of protein sequences, ligand SMILES and binding affinities."""
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VERSION = datasets.Version("1.4.1")
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def _info(self):
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# TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
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#if self.config.name == "first_domain": # This is the name of the configuration selected in BUILDER_CONFIGS above
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# features = datasets.Features(
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# {
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# "sentence": datasets.Value("string"),
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# "option1": datasets.Value("string"),
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# "answer": datasets.Value("string")
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# # These are the features of your dataset like images, labels ...
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# }
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# )
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#else: # This is an example to show how to have different features for "first_domain" and "second_domain"
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features = datasets.Features(
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{
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"seq": datasets.Value("string"),
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"smiles": datasets.Value("string"),
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"affinity_uM": datasets.Value("float"),
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"neg_log10_affinity_M": datasets.Value("float"),
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"smiles_can": datasets.Value("string"),
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"affinity": datasets.Value("float"),
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# These are the features of your dataset like images, labels ...
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}
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)
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return datasets.DatasetInfo(
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# This is the description that will appear on the datasets page.
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description=_DESCRIPTION,
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# This defines the different columns of the dataset and their types
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features=features, # Here we define them above because they are different between the two configurations
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# If there's a common (input, target) tuple from the features,
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# specify them here. They'll be used if as_supervised=True in
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# builder.as_dataset.
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supervised_keys=None,
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# Homepage of the dataset for documentation
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homepage=_HOMEPAGE,
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# License for the dataset if available
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license=_LICENSE,
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# Citation for the dataset
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citation=_CITATION,
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)
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def _split_generators(self, dl_manager):
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"""Returns SplitGenerators."""
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# TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
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# If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
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# dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLs
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# It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
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# By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
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files = dl_manager.download_and_extract(_URLs)
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return [
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datasets.SplitGenerator(
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# These kwargs will be passed to _generate_examples
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name=datasets.Split.TRAIN,
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gen_kwargs={
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'filepath': files["default"],
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},
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),
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datasets.SplitGenerator(
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name='no_kras',
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# These kwargs will be passed to _generate_examples
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gen_kwargs={
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"filepath": files["no_kras"],
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},
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),
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datasets.SplitGenerator(
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name='covalent',
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# These kwargs will be passed to _generate_examples
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gen_kwargs={
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"filepath": files["cov"],
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},
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),
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]
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def _generate_tables(
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self, filepath
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):
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from pyarrow import fs
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local = fs.LocalFileSystem()
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for i, f in enumerate([filepath]):
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yield i, pq.read_table(f,filesystem=local)
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bindingdb.ipynb
DELETED
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@@ -1,1258 +0,0 @@
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "ecce356e-321b-441e-8a5d-a20bf72f8691",
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"metadata": {},
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"outputs": [],
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| 9 |
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"source": [
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"import dask.dataframe as dd"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "89cbcd82-4ca2-4aba-95b7-e58c0ceed770",
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"metadata": {},
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| 18 |
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"outputs": [],
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| 19 |
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"source": [
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| 20 |
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"cols = ['Ligand SMILES', 'IC50 (nM)','KEGG ID of Ligand','Ki (nM)', 'Kd (nM)','EC50 (nM)']"
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| 21 |
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]
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| 22 |
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "a870d8d7-374b-4474-b9ee-305bbf9f17a9",
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"metadata": {},
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"outputs": [],
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"source": [
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"import tqdm.notebook"
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "e9f76b32-e8f0-47ee-b592-a91a88f4f93e",
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"metadata": {},
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"outputs": [
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| 39 |
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "c988bc89781242ec8c8b7f8fd0b1c233",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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" 0%| | 0/13 [00:00<?, ?it/s]"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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| 52 |
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}
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| 53 |
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],
|
| 54 |
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"source": [
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| 55 |
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"for i in tqdm.notebook.tqdm(range(0,13)):\n",
|
| 56 |
-
" mycol = 'BindingDB Target Chain Sequence.{}'.format(i)\n",
|
| 57 |
-
" allseq = ['BindingDB Target Chain Sequence']+['BindingDB Target Chain Sequence.{}'.format(j) for j in range(1,13)]\n",
|
| 58 |
-
" dtypes = {'BindingDB Target Chain Sequence.{}'.format(i): 'object' for i in range(1,13)}\n",
|
| 59 |
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" dtypes.update({'BindingDB Target Chain Sequence': 'object',\n",
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| 60 |
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" 'IC50 (nM)': 'object',\n",
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| 61 |
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" 'KEGG ID of Ligand': 'object',\n",
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| 62 |
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" 'Ki (nM)': 'object',\n",
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| 63 |
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" 'Kd (nM)': 'object',\n",
|
| 64 |
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" 'EC50 (nM)': 'object',\n",
|
| 65 |
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" 'koff (s-1)': 'object'})\n",
|
| 66 |
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" ddf = dd.read_csv('bindingdb/data/BindingDB_All.tsv',sep='\\t',error_bad_lines=False,blocksize=16*1024*1024,\n",
|
| 67 |
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" usecols=cols+allseq,\n",
|
| 68 |
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" dtype=dtypes)\n",
|
| 69 |
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" ddf = ddf.reset_index()\n",
|
| 70 |
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" ddf = ddf.rename(columns={'BindingDB Target Chain Sequence.{}'.format(j): 'seq_{}'.format(j) for j in range(1,13)})\n",
|
| 71 |
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" ddf = ddf.rename(columns={'BindingDB Target Chain Sequence': 'seq_0'})\n",
|
| 72 |
-
" ddf = ddf.drop(columns={'seq_{}'.format(j) for j in range(0,13) if i != j})\n",
|
| 73 |
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" ddf[cols+['seq_{}'.format(i)]].to_parquet('bindingdb/parquet_data/target{}'.format(i),schema='infer')"
|
| 74 |
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]
|
| 75 |
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},
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| 76 |
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{
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| 77 |
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"cell_type": "code",
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| 78 |
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"execution_count": 15,
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"id": "be79bbcf-0622-4d1e-8f08-a723a4167d8b",
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| 80 |
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"metadata": {},
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| 81 |
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"outputs": [],
|
| 82 |
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"source": [
|
| 83 |
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"ddfs = []\n",
|
| 84 |
-
"for i in range(0,13):\n",
|
| 85 |
-
" ddf = dd.read_parquet('bindingdb/parquet_data/target{}'.format(i))\n",
|
| 86 |
-
" ddf = ddf.rename(columns={'seq_{}'.format(i): 'seq'})\n",
|
| 87 |
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" ddfs.append(ddf)"
|
| 88 |
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]
|
| 89 |
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},
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| 90 |
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{
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"cell_type": "code",
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"execution_count": 16,
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"id": "35ca09cb-6264-4526-b504-0d29236a03c1",
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"metadata": {},
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| 95 |
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"outputs": [],
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"source": [
|
| 97 |
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"ddf = dd.concat(ddfs)"
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]
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},
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{
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"cell_type": "code",
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| 102 |
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"execution_count": 17,
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"id": "ba518a9a-0d15-47be-977b-e2dfe2511529",
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"metadata": {},
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| 105 |
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"outputs": [
|
| 106 |
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{
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"data": {
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| 108 |
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"text/html": [
|
| 109 |
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"<div>\n",
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| 110 |
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"<style scoped>\n",
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| 111 |
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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| 115 |
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" .dataframe tbody tr th {\n",
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| 116 |
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" vertical-align: top;\n",
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" }\n",
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"\n",
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| 119 |
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" .dataframe thead th {\n",
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" text-align: right;\n",
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| 121 |
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" }\n",
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| 122 |
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"</style>\n",
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| 123 |
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"<table border=\"1\" class=\"dataframe\">\n",
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| 124 |
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" <thead>\n",
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| 125 |
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" <tr style=\"text-align: right;\">\n",
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| 126 |
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" <th></th>\n",
|
| 127 |
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" <th>Ligand SMILES</th>\n",
|
| 128 |
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" <th>IC50 (nM)</th>\n",
|
| 129 |
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" <th>KEGG ID of Ligand</th>\n",
|
| 130 |
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" <th>Ki (nM)</th>\n",
|
| 131 |
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" <th>Kd (nM)</th>\n",
|
| 132 |
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" <th>EC50 (nM)</th>\n",
|
| 133 |
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" <th>seq</th>\n",
|
| 134 |
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" </tr>\n",
|
| 135 |
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" </thead>\n",
|
| 136 |
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" <tbody>\n",
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| 137 |
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" <tr>\n",
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| 138 |
-
" <th>0</th>\n",
|
| 139 |
-
" <td>COc1cc2c(Nc3ccc(Br)cc3F)ncnc2cc1OCC1CCN(C)CC1</td>\n",
|
| 140 |
-
" <td>None</td>\n",
|
| 141 |
-
" <td>None</td>\n",
|
| 142 |
-
" <td>0.24</td>\n",
|
| 143 |
-
" <td>None</td>\n",
|
| 144 |
-
" <td>None</td>\n",
|
| 145 |
-
" <td>PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKM...</td>\n",
|
| 146 |
-
" </tr>\n",
|
| 147 |
-
" <tr>\n",
|
| 148 |
-
" <th>1</th>\n",
|
| 149 |
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" <td>O[C@@H]1[C@@H](O)[C@@H](Cc2ccccc2)N(C\\C=C\\c2cn...</td>\n",
|
| 150 |
-
" <td>None</td>\n",
|
| 151 |
-
" <td>None</td>\n",
|
| 152 |
-
" <td>0.25</td>\n",
|
| 153 |
-
" <td>None</td>\n",
|
| 154 |
-
" <td>None</td>\n",
|
| 155 |
-
" <td>PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKM...</td>\n",
|
| 156 |
-
" </tr>\n",
|
| 157 |
-
" <tr>\n",
|
| 158 |
-
" <th>2</th>\n",
|
| 159 |
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" <td>O[C@@H]1[C@@H](O)[C@@H](Cc2ccccc2)N(CC2CC2)C(=...</td>\n",
|
| 160 |
-
" <td>None</td>\n",
|
| 161 |
-
" <td>None</td>\n",
|
| 162 |
-
" <td>0.41</td>\n",
|
| 163 |
-
" <td>None</td>\n",
|
| 164 |
-
" <td>None</td>\n",
|
| 165 |
-
" <td>PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKM...</td>\n",
|
| 166 |
-
" </tr>\n",
|
| 167 |
-
" <tr>\n",
|
| 168 |
-
" <th>3</th>\n",
|
| 169 |
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" <td>OCCCCCCN1[C@H](Cc2ccccc2)[C@H](O)[C@@H](O)[C@@...</td>\n",
|
| 170 |
-
" <td>None</td>\n",
|
| 171 |
-
" <td>None</td>\n",
|
| 172 |
-
" <td>0.8</td>\n",
|
| 173 |
-
" <td>None</td>\n",
|
| 174 |
-
" <td>None</td>\n",
|
| 175 |
-
" <td>PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKM...</td>\n",
|
| 176 |
-
" </tr>\n",
|
| 177 |
-
" <tr>\n",
|
| 178 |
-
" <th>4</th>\n",
|
| 179 |
-
" <td>OCCCCCN1[C@H](Cc2ccccc2)[C@H](O)[C@@H](O)[C@@H...</td>\n",
|
| 180 |
-
" <td>None</td>\n",
|
| 181 |
-
" <td>None</td>\n",
|
| 182 |
-
" <td>0.99</td>\n",
|
| 183 |
-
" <td>None</td>\n",
|
| 184 |
-
" <td>None</td>\n",
|
| 185 |
-
" <td>PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKM...</td>\n",
|
| 186 |
-
" </tr>\n",
|
| 187 |
-
" </tbody>\n",
|
| 188 |
-
"</table>\n",
|
| 189 |
-
"</div>"
|
| 190 |
-
],
|
| 191 |
-
"text/plain": [
|
| 192 |
-
" Ligand SMILES IC50 (nM) \\\n",
|
| 193 |
-
"0 COc1cc2c(Nc3ccc(Br)cc3F)ncnc2cc1OCC1CCN(C)CC1 None \n",
|
| 194 |
-
"1 O[C@@H]1[C@@H](O)[C@@H](Cc2ccccc2)N(C\\C=C\\c2cn... None \n",
|
| 195 |
-
"2 O[C@@H]1[C@@H](O)[C@@H](Cc2ccccc2)N(CC2CC2)C(=... None \n",
|
| 196 |
-
"3 OCCCCCCN1[C@H](Cc2ccccc2)[C@H](O)[C@@H](O)[C@@... None \n",
|
| 197 |
-
"4 OCCCCCN1[C@H](Cc2ccccc2)[C@H](O)[C@@H](O)[C@@H... None \n",
|
| 198 |
-
"\n",
|
| 199 |
-
" KEGG ID of Ligand Ki (nM) Kd (nM) EC50 (nM) \\\n",
|
| 200 |
-
"0 None 0.24 None None \n",
|
| 201 |
-
"1 None 0.25 None None \n",
|
| 202 |
-
"2 None 0.41 None None \n",
|
| 203 |
-
"3 None 0.8 None None \n",
|
| 204 |
-
"4 None 0.99 None None \n",
|
| 205 |
-
"\n",
|
| 206 |
-
" seq \n",
|
| 207 |
-
"0 PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKM... \n",
|
| 208 |
-
"1 PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKM... \n",
|
| 209 |
-
"2 PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKM... \n",
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| 210 |
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"3 PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKM... \n",
|
| 211 |
-
"4 PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKM... "
|
| 212 |
-
]
|
| 213 |
-
},
|
| 214 |
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"execution_count": 17,
|
| 215 |
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"metadata": {},
|
| 216 |
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"output_type": "execute_result"
|
| 217 |
-
}
|
| 218 |
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],
|
| 219 |
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"source": [
|
| 220 |
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"ddf.head()"
|
| 221 |
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]
|
| 222 |
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},
|
| 223 |
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{
|
| 224 |
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"cell_type": "code",
|
| 225 |
-
"execution_count": 9,
|
| 226 |
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"id": "f504d7aa-dfc1-4346-a136-8814c4b5d979",
|
| 227 |
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"metadata": {},
|
| 228 |
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"outputs": [],
|
| 229 |
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"source": [
|
| 230 |
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"ddf.repartition(partition_size='25MB').to_parquet('bindingdb/parquet_data/all_targets',schema='infer')"
|
| 231 |
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]
|
| 232 |
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},
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| 233 |
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{
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| 234 |
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"cell_type": "code",
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| 235 |
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"execution_count": 4,
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| 236 |
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"id": "d7eafa69-4606-4b34-ae8f-8c6462dcb004",
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| 237 |
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"metadata": {},
|
| 238 |
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"outputs": [],
|
| 239 |
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"source": [
|
| 240 |
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"ddf = dd.read_parquet('../binding_affinity/bindingdb/parquet_data/all_targets')"
|
| 241 |
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]
|
| 242 |
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},
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| 243 |
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "b151868a-0cd6-405e-8401-f79918fb0b07",
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| 247 |
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"metadata": {},
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| 248 |
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"outputs": [
|
| 249 |
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{
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| 250 |
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"data": {
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"text/html": [
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| 252 |
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"<div><strong>Dask DataFrame Structure:</strong></div>\n",
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| 253 |
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" }\n",
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"\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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| 266 |
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"</style>\n",
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| 267 |
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"<table border=\"1\" class=\"dataframe\">\n",
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| 268 |
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" <thead>\n",
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| 269 |
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" <tr style=\"text-align: right;\">\n",
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| 270 |
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" <th></th>\n",
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| 271 |
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" <th>Ligand SMILES</th>\n",
|
| 272 |
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" <th>IC50 (nM)</th>\n",
|
| 273 |
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" <th>KEGG ID of Ligand</th>\n",
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| 274 |
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" <th>Ki (nM)</th>\n",
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| 275 |
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" <th>Kd (nM)</th>\n",
|
| 276 |
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" <th>EC50 (nM)</th>\n",
|
| 277 |
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" <th>seq</th>\n",
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| 278 |
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" </tr>\n",
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| 279 |
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" <th>npartitions=483</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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| 292 |
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" <th></th>\n",
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| 293 |
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" <td>object</td>\n",
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" <td>object</td>\n",
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" <td>object</td>\n",
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" <td>object</td>\n",
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" <td>object</td>\n",
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" <td>object</td>\n",
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" <td>object</td>\n",
|
| 300 |
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" </tr>\n",
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" <tr>\n",
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" <th></th>\n",
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" <td>...</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>...</th>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" </tbody>\n",
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| 342 |
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"</table>\n",
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| 343 |
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"</div>\n",
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| 344 |
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"<div>Dask Name: read-parquet, 483 tasks</div>"
|
| 345 |
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],
|
| 346 |
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"text/plain": [
|
| 347 |
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"Dask DataFrame Structure:\n",
|
| 348 |
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" Ligand SMILES IC50 (nM) KEGG ID of Ligand Ki (nM) Kd (nM) EC50 (nM) seq\n",
|
| 349 |
-
"npartitions=483 \n",
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| 350 |
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" object object object object object object object\n",
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| 351 |
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" ... ... ... ... ... ... ...\n",
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"... ... ... ... ... ... ... ...\n",
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" ... ... ... ... ... ... ...\n",
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| 354 |
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" ... ... ... ... ... ... ...\n",
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| 355 |
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"Dask Name: read-parquet, 483 tasks"
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| 356 |
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]
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| 357 |
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},
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| 358 |
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"execution_count": 5,
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"metadata": {},
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| 360 |
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"output_type": "execute_result"
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| 361 |
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}
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| 362 |
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],
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| 363 |
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"source": [
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| 364 |
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"ddf"
|
| 365 |
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]
|
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},
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| 367 |
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{
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| 368 |
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"cell_type": "code",
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| 369 |
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"execution_count": 6,
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| 370 |
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"id": "c00102b8-f4be-4ebd-8d30-7a2c7fc2d05e",
|
| 371 |
-
"metadata": {},
|
| 372 |
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"outputs": [],
|
| 373 |
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"source": [
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| 374 |
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"ddf_nonnull = ddf[~ddf.seq.isnull()].copy()"
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| 375 |
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]
|
| 376 |
-
},
|
| 377 |
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{
|
| 378 |
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"cell_type": "code",
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| 379 |
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"execution_count": 8,
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| 380 |
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"id": "c5337e06-1e45-4180-90ed-49ac9ecdd24a",
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| 381 |
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"metadata": {},
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| 382 |
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"outputs": [
|
| 383 |
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{
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| 384 |
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"data": {
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| 385 |
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"text/html": [
|
| 386 |
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"<div>\n",
|
| 387 |
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"<style scoped>\n",
|
| 388 |
-
" .dataframe tbody tr th:only-of-type {\n",
|
| 389 |
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" vertical-align: middle;\n",
|
| 390 |
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" }\n",
|
| 391 |
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"\n",
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| 392 |
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" .dataframe tbody tr th {\n",
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| 393 |
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" text-align: right;\n",
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" }\n",
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| 399 |
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"</style>\n",
|
| 400 |
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"<table border=\"1\" class=\"dataframe\">\n",
|
| 401 |
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" <thead>\n",
|
| 402 |
-
" <tr style=\"text-align: right;\">\n",
|
| 403 |
-
" <th></th>\n",
|
| 404 |
-
" <th>Ligand SMILES</th>\n",
|
| 405 |
-
" <th>IC50 (nM)</th>\n",
|
| 406 |
-
" <th>KEGG ID of Ligand</th>\n",
|
| 407 |
-
" <th>Ki (nM)</th>\n",
|
| 408 |
-
" <th>Kd (nM)</th>\n",
|
| 409 |
-
" <th>EC50 (nM)</th>\n",
|
| 410 |
-
" <th>seq</th>\n",
|
| 411 |
-
" </tr>\n",
|
| 412 |
-
" </thead>\n",
|
| 413 |
-
" <tbody>\n",
|
| 414 |
-
" <tr>\n",
|
| 415 |
-
" <th>0</th>\n",
|
| 416 |
-
" <td>COc1cc2c(Nc3ccc(Br)cc3F)ncnc2cc1OCC1CCN(C)CC1</td>\n",
|
| 417 |
-
" <td>None</td>\n",
|
| 418 |
-
" <td>None</td>\n",
|
| 419 |
-
" <td>0.24</td>\n",
|
| 420 |
-
" <td>None</td>\n",
|
| 421 |
-
" <td>None</td>\n",
|
| 422 |
-
" <td>PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKM...</td>\n",
|
| 423 |
-
" </tr>\n",
|
| 424 |
-
" <tr>\n",
|
| 425 |
-
" <th>1</th>\n",
|
| 426 |
-
" <td>O[C@@H]1[C@@H](O)[C@@H](Cc2ccccc2)N(C\\C=C\\c2cn...</td>\n",
|
| 427 |
-
" <td>None</td>\n",
|
| 428 |
-
" <td>None</td>\n",
|
| 429 |
-
" <td>0.25</td>\n",
|
| 430 |
-
" <td>None</td>\n",
|
| 431 |
-
" <td>None</td>\n",
|
| 432 |
-
" <td>PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKM...</td>\n",
|
| 433 |
-
" </tr>\n",
|
| 434 |
-
" <tr>\n",
|
| 435 |
-
" <th>2</th>\n",
|
| 436 |
-
" <td>O[C@@H]1[C@@H](O)[C@@H](Cc2ccccc2)N(CC2CC2)C(=...</td>\n",
|
| 437 |
-
" <td>None</td>\n",
|
| 438 |
-
" <td>None</td>\n",
|
| 439 |
-
" <td>0.41</td>\n",
|
| 440 |
-
" <td>None</td>\n",
|
| 441 |
-
" <td>None</td>\n",
|
| 442 |
-
" <td>PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKM...</td>\n",
|
| 443 |
-
" </tr>\n",
|
| 444 |
-
" <tr>\n",
|
| 445 |
-
" <th>3</th>\n",
|
| 446 |
-
" <td>OCCCCCCN1[C@H](Cc2ccccc2)[C@H](O)[C@@H](O)[C@@...</td>\n",
|
| 447 |
-
" <td>None</td>\n",
|
| 448 |
-
" <td>None</td>\n",
|
| 449 |
-
" <td>0.8</td>\n",
|
| 450 |
-
" <td>None</td>\n",
|
| 451 |
-
" <td>None</td>\n",
|
| 452 |
-
" <td>PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKM...</td>\n",
|
| 453 |
-
" </tr>\n",
|
| 454 |
-
" <tr>\n",
|
| 455 |
-
" <th>4</th>\n",
|
| 456 |
-
" <td>OCCCCCN1[C@H](Cc2ccccc2)[C@H](O)[C@@H](O)[C@@H...</td>\n",
|
| 457 |
-
" <td>None</td>\n",
|
| 458 |
-
" <td>None</td>\n",
|
| 459 |
-
" <td>0.99</td>\n",
|
| 460 |
-
" <td>None</td>\n",
|
| 461 |
-
" <td>None</td>\n",
|
| 462 |
-
" <td>PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKM...</td>\n",
|
| 463 |
-
" </tr>\n",
|
| 464 |
-
" </tbody>\n",
|
| 465 |
-
"</table>\n",
|
| 466 |
-
"</div>"
|
| 467 |
-
],
|
| 468 |
-
"text/plain": [
|
| 469 |
-
" Ligand SMILES IC50 (nM) \\\n",
|
| 470 |
-
"0 COc1cc2c(Nc3ccc(Br)cc3F)ncnc2cc1OCC1CCN(C)CC1 None \n",
|
| 471 |
-
"1 O[C@@H]1[C@@H](O)[C@@H](Cc2ccccc2)N(C\\C=C\\c2cn... None \n",
|
| 472 |
-
"2 O[C@@H]1[C@@H](O)[C@@H](Cc2ccccc2)N(CC2CC2)C(=... None \n",
|
| 473 |
-
"3 OCCCCCCN1[C@H](Cc2ccccc2)[C@H](O)[C@@H](O)[C@@... None \n",
|
| 474 |
-
"4 OCCCCCN1[C@H](Cc2ccccc2)[C@H](O)[C@@H](O)[C@@H... None \n",
|
| 475 |
-
"\n",
|
| 476 |
-
" KEGG ID of Ligand Ki (nM) Kd (nM) EC50 (nM) \\\n",
|
| 477 |
-
"0 None 0.24 None None \n",
|
| 478 |
-
"1 None 0.25 None None \n",
|
| 479 |
-
"2 None 0.41 None None \n",
|
| 480 |
-
"3 None 0.8 None None \n",
|
| 481 |
-
"4 None 0.99 None None \n",
|
| 482 |
-
"\n",
|
| 483 |
-
" seq \n",
|
| 484 |
-
"0 PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKM... \n",
|
| 485 |
-
"1 PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKM... \n",
|
| 486 |
-
"2 PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKM... \n",
|
| 487 |
-
"3 PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKM... \n",
|
| 488 |
-
"4 PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKM... "
|
| 489 |
-
]
|
| 490 |
-
},
|
| 491 |
-
"execution_count": 8,
|
| 492 |
-
"metadata": {},
|
| 493 |
-
"output_type": "execute_result"
|
| 494 |
-
}
|
| 495 |
-
],
|
| 496 |
-
"source": [
|
| 497 |
-
"ddf_nonnull.head()"
|
| 498 |
-
]
|
| 499 |
-
},
|
| 500 |
-
{
|
| 501 |
-
"cell_type": "code",
|
| 502 |
-
"execution_count": 17,
|
| 503 |
-
"id": "7b423365-4989-4325-a5a5-845d852d52e9",
|
| 504 |
-
"metadata": {},
|
| 505 |
-
"outputs": [
|
| 506 |
-
{
|
| 507 |
-
"data": {
|
| 508 |
-
"text/plain": [
|
| 509 |
-
"2512985"
|
| 510 |
-
]
|
| 511 |
-
},
|
| 512 |
-
"execution_count": 17,
|
| 513 |
-
"metadata": {},
|
| 514 |
-
"output_type": "execute_result"
|
| 515 |
-
}
|
| 516 |
-
],
|
| 517 |
-
"source": [
|
| 518 |
-
"len(ddf_nonnull)"
|
| 519 |
-
]
|
| 520 |
-
},
|
| 521 |
-
{
|
| 522 |
-
"cell_type": "code",
|
| 523 |
-
"execution_count": 11,
|
| 524 |
-
"id": "872edb84-3459-43d9-8e0e-e2a6b5d281eb",
|
| 525 |
-
"metadata": {},
|
| 526 |
-
"outputs": [],
|
| 527 |
-
"source": [
|
| 528 |
-
"from pint import UnitRegistry\n",
|
| 529 |
-
"import numpy as np\n",
|
| 530 |
-
"import re\n",
|
| 531 |
-
"ureg = UnitRegistry()\n",
|
| 532 |
-
"\n",
|
| 533 |
-
"def to_uM(affinities):\n",
|
| 534 |
-
" ic50, Ki, Kd, ec50 = affinities\n",
|
| 535 |
-
"\n",
|
| 536 |
-
" vals = []\n",
|
| 537 |
-
" \n",
|
| 538 |
-
" try:\n",
|
| 539 |
-
" ic50 = ureg(str(ic50)+'nM').m_as(ureg.uM)\n",
|
| 540 |
-
" vals.append(ic50)\n",
|
| 541 |
-
" except:\n",
|
| 542 |
-
" pass\n",
|
| 543 |
-
"\n",
|
| 544 |
-
" try:\n",
|
| 545 |
-
" Ki = ureg(str(Ki)+'nM').m_as(ureg.uM)\n",
|
| 546 |
-
" vals.append(Ki)\n",
|
| 547 |
-
" except:\n",
|
| 548 |
-
" pass\n",
|
| 549 |
-
"\n",
|
| 550 |
-
" try:\n",
|
| 551 |
-
" Kd = ureg(str(Kd)+'nM').m_as(ureg.uM)\n",
|
| 552 |
-
" vals.append(Kd)\n",
|
| 553 |
-
" except:\n",
|
| 554 |
-
" pass\n",
|
| 555 |
-
"\n",
|
| 556 |
-
" try:\n",
|
| 557 |
-
" ec50 = ureg(str(ec50)+'nM').m_as(ureg.uM)\n",
|
| 558 |
-
" vals.append(ec50)\n",
|
| 559 |
-
" except:\n",
|
| 560 |
-
" pass\n",
|
| 561 |
-
"\n",
|
| 562 |
-
" if len(vals) > 0:\n",
|
| 563 |
-
" vals = np.array(vals)\n",
|
| 564 |
-
" return np.mean(vals[~np.isnan(vals)])\n",
|
| 565 |
-
" \n",
|
| 566 |
-
" return None"
|
| 567 |
-
]
|
| 568 |
-
},
|
| 569 |
-
{
|
| 570 |
-
"cell_type": "code",
|
| 571 |
-
"execution_count": 12,
|
| 572 |
-
"id": "b3cff13c-19b2-4413-a84b-d99062f516a7",
|
| 573 |
-
"metadata": {},
|
| 574 |
-
"outputs": [],
|
| 575 |
-
"source": [
|
| 576 |
-
"df_nonnull = ddf_nonnull.compute()"
|
| 577 |
-
]
|
| 578 |
-
},
|
| 579 |
-
{
|
| 580 |
-
"cell_type": "code",
|
| 581 |
-
"execution_count": 13,
|
| 582 |
-
"id": "ca9795de-e821-4dc3-a7bf-70ade9e4c7f0",
|
| 583 |
-
"metadata": {},
|
| 584 |
-
"outputs": [
|
| 585 |
-
{
|
| 586 |
-
"name": "stdout",
|
| 587 |
-
"output_type": "stream",
|
| 588 |
-
"text": [
|
| 589 |
-
"INFO: Pandarallel will run on 32 workers.\n",
|
| 590 |
-
"INFO: Pandarallel will use Memory file system to transfer data between the main process and workers.\n"
|
| 591 |
-
]
|
| 592 |
-
}
|
| 593 |
-
],
|
| 594 |
-
"source": [
|
| 595 |
-
"from pandarallel import pandarallel\n",
|
| 596 |
-
"pandarallel.initialize()\n"
|
| 597 |
-
]
|
| 598 |
-
},
|
| 599 |
-
{
|
| 600 |
-
"cell_type": "code",
|
| 601 |
-
"execution_count": 14,
|
| 602 |
-
"id": "4356a3e2-fede-48e7-a486-343661fe0a0a",
|
| 603 |
-
"metadata": {},
|
| 604 |
-
"outputs": [],
|
| 605 |
-
"source": [
|
| 606 |
-
"df_affinity = df_nonnull.copy()\n",
|
| 607 |
-
"df_affinity['affinity_uM'] = df_affinity[['IC50 (nM)', 'Ki (nM)', 'Kd (nM)','EC50 (nM)']].parallel_apply(to_uM,axis=1)"
|
| 608 |
-
]
|
| 609 |
-
},
|
| 610 |
-
{
|
| 611 |
-
"cell_type": "code",
|
| 612 |
-
"execution_count": 15,
|
| 613 |
-
"id": "e91c3af8-84a5-42a2-9e25-49cb2f320b0b",
|
| 614 |
-
"metadata": {},
|
| 615 |
-
"outputs": [],
|
| 616 |
-
"source": [
|
| 617 |
-
"df_affinity[~df_affinity['affinity_uM'].isnull()].to_parquet('data/bindingdb.parquet')"
|
| 618 |
-
]
|
| 619 |
-
},
|
| 620 |
-
{
|
| 621 |
-
"cell_type": "code",
|
| 622 |
-
"execution_count": 1,
|
| 623 |
-
"id": "f3a9173e-d574-4314-9cea-f8c0a66766c0",
|
| 624 |
-
"metadata": {},
|
| 625 |
-
"outputs": [],
|
| 626 |
-
"source": [
|
| 627 |
-
"import pandas as pd\n",
|
| 628 |
-
"df_affinity = pd.read_parquet('data/bindingdb.parquet')"
|
| 629 |
-
]
|
| 630 |
-
},
|
| 631 |
-
{
|
| 632 |
-
"cell_type": "code",
|
| 633 |
-
"execution_count": 2,
|
| 634 |
-
"id": "f602fdbe-7083-436c-9eac-9d97fbc8be67",
|
| 635 |
-
"metadata": {},
|
| 636 |
-
"outputs": [
|
| 637 |
-
{
|
| 638 |
-
"data": {
|
| 639 |
-
"text/plain": [
|
| 640 |
-
"2510716"
|
| 641 |
-
]
|
| 642 |
-
},
|
| 643 |
-
"execution_count": 2,
|
| 644 |
-
"metadata": {},
|
| 645 |
-
"output_type": "execute_result"
|
| 646 |
-
}
|
| 647 |
-
],
|
| 648 |
-
"source": [
|
| 649 |
-
"len(df_affinity)"
|
| 650 |
-
]
|
| 651 |
-
},
|
| 652 |
-
{
|
| 653 |
-
"cell_type": "code",
|
| 654 |
-
"execution_count": 3,
|
| 655 |
-
"id": "27194288-cf3e-4c30-ad55-3b0998fdf939",
|
| 656 |
-
"metadata": {},
|
| 657 |
-
"outputs": [
|
| 658 |
-
{
|
| 659 |
-
"data": {
|
| 660 |
-
"text/html": [
|
| 661 |
-
"<div>\n",
|
| 662 |
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"<style scoped>\n",
|
| 663 |
-
" .dataframe tbody tr th:only-of-type {\n",
|
| 664 |
-
" vertical-align: middle;\n",
|
| 665 |
-
" }\n",
|
| 666 |
-
"\n",
|
| 667 |
-
" .dataframe tbody tr th {\n",
|
| 668 |
-
" vertical-align: top;\n",
|
| 669 |
-
" }\n",
|
| 670 |
-
"\n",
|
| 671 |
-
" .dataframe thead th {\n",
|
| 672 |
-
" text-align: right;\n",
|
| 673 |
-
" }\n",
|
| 674 |
-
"</style>\n",
|
| 675 |
-
"<table border=\"1\" class=\"dataframe\">\n",
|
| 676 |
-
" <thead>\n",
|
| 677 |
-
" <tr style=\"text-align: right;\">\n",
|
| 678 |
-
" <th></th>\n",
|
| 679 |
-
" <th>Ligand SMILES</th>\n",
|
| 680 |
-
" <th>IC50 (nM)</th>\n",
|
| 681 |
-
" <th>KEGG ID of Ligand</th>\n",
|
| 682 |
-
" <th>Ki (nM)</th>\n",
|
| 683 |
-
" <th>Kd (nM)</th>\n",
|
| 684 |
-
" <th>EC50 (nM)</th>\n",
|
| 685 |
-
" <th>seq</th>\n",
|
| 686 |
-
" <th>affinity_uM</th>\n",
|
| 687 |
-
" </tr>\n",
|
| 688 |
-
" </thead>\n",
|
| 689 |
-
" <tbody>\n",
|
| 690 |
-
" <tr>\n",
|
| 691 |
-
" <th>0</th>\n",
|
| 692 |
-
" <td>COc1cc2c(Nc3ccc(Br)cc3F)ncnc2cc1OCC1CCN(C)CC1</td>\n",
|
| 693 |
-
" <td>None</td>\n",
|
| 694 |
-
" <td>None</td>\n",
|
| 695 |
-
" <td>0.24</td>\n",
|
| 696 |
-
" <td>None</td>\n",
|
| 697 |
-
" <td>None</td>\n",
|
| 698 |
-
" <td>PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKM...</td>\n",
|
| 699 |
-
" <td>0.00024</td>\n",
|
| 700 |
-
" </tr>\n",
|
| 701 |
-
" <tr>\n",
|
| 702 |
-
" <th>1</th>\n",
|
| 703 |
-
" <td>O[C@@H]1[C@@H](O)[C@@H](Cc2ccccc2)N(C\\C=C\\c2cn...</td>\n",
|
| 704 |
-
" <td>None</td>\n",
|
| 705 |
-
" <td>None</td>\n",
|
| 706 |
-
" <td>0.25</td>\n",
|
| 707 |
-
" <td>None</td>\n",
|
| 708 |
-
" <td>None</td>\n",
|
| 709 |
-
" <td>PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKM...</td>\n",
|
| 710 |
-
" <td>0.00025</td>\n",
|
| 711 |
-
" </tr>\n",
|
| 712 |
-
" <tr>\n",
|
| 713 |
-
" <th>2</th>\n",
|
| 714 |
-
" <td>O[C@@H]1[C@@H](O)[C@@H](Cc2ccccc2)N(CC2CC2)C(=...</td>\n",
|
| 715 |
-
" <td>None</td>\n",
|
| 716 |
-
" <td>None</td>\n",
|
| 717 |
-
" <td>0.41</td>\n",
|
| 718 |
-
" <td>None</td>\n",
|
| 719 |
-
" <td>None</td>\n",
|
| 720 |
-
" <td>PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKM...</td>\n",
|
| 721 |
-
" <td>0.00041</td>\n",
|
| 722 |
-
" </tr>\n",
|
| 723 |
-
" <tr>\n",
|
| 724 |
-
" <th>3</th>\n",
|
| 725 |
-
" <td>OCCCCCCN1[C@H](Cc2ccccc2)[C@H](O)[C@@H](O)[C@@...</td>\n",
|
| 726 |
-
" <td>None</td>\n",
|
| 727 |
-
" <td>None</td>\n",
|
| 728 |
-
" <td>0.8</td>\n",
|
| 729 |
-
" <td>None</td>\n",
|
| 730 |
-
" <td>None</td>\n",
|
| 731 |
-
" <td>PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKM...</td>\n",
|
| 732 |
-
" <td>0.00080</td>\n",
|
| 733 |
-
" </tr>\n",
|
| 734 |
-
" <tr>\n",
|
| 735 |
-
" <th>4</th>\n",
|
| 736 |
-
" <td>OCCCCCN1[C@H](Cc2ccccc2)[C@H](O)[C@@H](O)[C@@H...</td>\n",
|
| 737 |
-
" <td>None</td>\n",
|
| 738 |
-
" <td>None</td>\n",
|
| 739 |
-
" <td>0.99</td>\n",
|
| 740 |
-
" <td>None</td>\n",
|
| 741 |
-
" <td>None</td>\n",
|
| 742 |
-
" <td>PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKM...</td>\n",
|
| 743 |
-
" <td>0.00099</td>\n",
|
| 744 |
-
" </tr>\n",
|
| 745 |
-
" </tbody>\n",
|
| 746 |
-
"</table>\n",
|
| 747 |
-
"</div>"
|
| 748 |
-
],
|
| 749 |
-
"text/plain": [
|
| 750 |
-
" Ligand SMILES IC50 (nM) \\\n",
|
| 751 |
-
"0 COc1cc2c(Nc3ccc(Br)cc3F)ncnc2cc1OCC1CCN(C)CC1 None \n",
|
| 752 |
-
"1 O[C@@H]1[C@@H](O)[C@@H](Cc2ccccc2)N(C\\C=C\\c2cn... None \n",
|
| 753 |
-
"2 O[C@@H]1[C@@H](O)[C@@H](Cc2ccccc2)N(CC2CC2)C(=... None \n",
|
| 754 |
-
"3 OCCCCCCN1[C@H](Cc2ccccc2)[C@H](O)[C@@H](O)[C@@... None \n",
|
| 755 |
-
"4 OCCCCCN1[C@H](Cc2ccccc2)[C@H](O)[C@@H](O)[C@@H... None \n",
|
| 756 |
-
"\n",
|
| 757 |
-
" KEGG ID of Ligand Ki (nM) Kd (nM) EC50 (nM) \\\n",
|
| 758 |
-
"0 None 0.24 None None \n",
|
| 759 |
-
"1 None 0.25 None None \n",
|
| 760 |
-
"2 None 0.41 None None \n",
|
| 761 |
-
"3 None 0.8 None None \n",
|
| 762 |
-
"4 None 0.99 None None \n",
|
| 763 |
-
"\n",
|
| 764 |
-
" seq affinity_uM \n",
|
| 765 |
-
"0 PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKM... 0.00024 \n",
|
| 766 |
-
"1 PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKM... 0.00025 \n",
|
| 767 |
-
"2 PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKM... 0.00041 \n",
|
| 768 |
-
"3 PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKM... 0.00080 \n",
|
| 769 |
-
"4 PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKM... 0.00099 "
|
| 770 |
-
]
|
| 771 |
-
},
|
| 772 |
-
"execution_count": 3,
|
| 773 |
-
"metadata": {},
|
| 774 |
-
"output_type": "execute_result"
|
| 775 |
-
}
|
| 776 |
-
],
|
| 777 |
-
"source": [
|
| 778 |
-
"df_affinity.head()"
|
| 779 |
-
]
|
| 780 |
-
},
|
| 781 |
-
{
|
| 782 |
-
"cell_type": "code",
|
| 783 |
-
"execution_count": 4,
|
| 784 |
-
"id": "603fd298-0aa6-4097-b298-c55db013548c",
|
| 785 |
-
"metadata": {},
|
| 786 |
-
"outputs": [
|
| 787 |
-
{
|
| 788 |
-
"data": {
|
| 789 |
-
"text/plain": [
|
| 790 |
-
"2510716"
|
| 791 |
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]
|
| 792 |
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},
|
| 793 |
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"execution_count": 4,
|
| 794 |
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"metadata": {},
|
| 795 |
-
"output_type": "execute_result"
|
| 796 |
-
}
|
| 797 |
-
],
|
| 798 |
-
"source": [
|
| 799 |
-
"len(df_affinity)"
|
| 800 |
-
]
|
| 801 |
-
},
|
| 802 |
-
{
|
| 803 |
-
"cell_type": "code",
|
| 804 |
-
"execution_count": 5,
|
| 805 |
-
"id": "d95ad9a9-d4ca-4679-8a33-235fe6e7047f",
|
| 806 |
-
"metadata": {},
|
| 807 |
-
"outputs": [
|
| 808 |
-
{
|
| 809 |
-
"data": {
|
| 810 |
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"text/plain": [
|
| 811 |
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"2510716"
|
| 812 |
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]
|
| 813 |
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},
|
| 814 |
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"execution_count": 5,
|
| 815 |
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"metadata": {},
|
| 816 |
-
"output_type": "execute_result"
|
| 817 |
-
}
|
| 818 |
-
],
|
| 819 |
-
"source": [
|
| 820 |
-
"len(df_affinity[~df_affinity['affinity_uM'].isnull()])"
|
| 821 |
-
]
|
| 822 |
-
},
|
| 823 |
-
{
|
| 824 |
-
"cell_type": "code",
|
| 825 |
-
"execution_count": 6,
|
| 826 |
-
"id": "b21c4683-0fe1-4a0a-a55c-c0dc80193f36",
|
| 827 |
-
"metadata": {},
|
| 828 |
-
"outputs": [
|
| 829 |
-
{
|
| 830 |
-
"data": {
|
| 831 |
-
"text/html": [
|
| 832 |
-
"<div>\n",
|
| 833 |
-
"<style scoped>\n",
|
| 834 |
-
" .dataframe tbody tr th:only-of-type {\n",
|
| 835 |
-
" vertical-align: middle;\n",
|
| 836 |
-
" }\n",
|
| 837 |
-
"\n",
|
| 838 |
-
" .dataframe tbody tr th {\n",
|
| 839 |
-
" vertical-align: top;\n",
|
| 840 |
-
" }\n",
|
| 841 |
-
"\n",
|
| 842 |
-
" .dataframe thead th {\n",
|
| 843 |
-
" text-align: right;\n",
|
| 844 |
-
" }\n",
|
| 845 |
-
"</style>\n",
|
| 846 |
-
"<table border=\"1\" class=\"dataframe\">\n",
|
| 847 |
-
" <thead>\n",
|
| 848 |
-
" <tr style=\"text-align: right;\">\n",
|
| 849 |
-
" <th></th>\n",
|
| 850 |
-
" <th>Ligand SMILES</th>\n",
|
| 851 |
-
" <th>IC50 (nM)</th>\n",
|
| 852 |
-
" <th>KEGG ID of Ligand</th>\n",
|
| 853 |
-
" <th>Ki (nM)</th>\n",
|
| 854 |
-
" <th>Kd (nM)</th>\n",
|
| 855 |
-
" <th>EC50 (nM)</th>\n",
|
| 856 |
-
" <th>seq</th>\n",
|
| 857 |
-
" <th>affinity_uM</th>\n",
|
| 858 |
-
" </tr>\n",
|
| 859 |
-
" </thead>\n",
|
| 860 |
-
" <tbody>\n",
|
| 861 |
-
" <tr>\n",
|
| 862 |
-
" <th>0</th>\n",
|
| 863 |
-
" <td>COc1cc2c(Nc3ccc(Br)cc3F)ncnc2cc1OCC1CCN(C)CC1</td>\n",
|
| 864 |
-
" <td>None</td>\n",
|
| 865 |
-
" <td>None</td>\n",
|
| 866 |
-
" <td>0.24</td>\n",
|
| 867 |
-
" <td>None</td>\n",
|
| 868 |
-
" <td>None</td>\n",
|
| 869 |
-
" <td>PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKM...</td>\n",
|
| 870 |
-
" <td>0.00024</td>\n",
|
| 871 |
-
" </tr>\n",
|
| 872 |
-
" <tr>\n",
|
| 873 |
-
" <th>1</th>\n",
|
| 874 |
-
" <td>O[C@@H]1[C@@H](O)[C@@H](Cc2ccccc2)N(C\\C=C\\c2cn...</td>\n",
|
| 875 |
-
" <td>None</td>\n",
|
| 876 |
-
" <td>None</td>\n",
|
| 877 |
-
" <td>0.25</td>\n",
|
| 878 |
-
" <td>None</td>\n",
|
| 879 |
-
" <td>None</td>\n",
|
| 880 |
-
" <td>PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKM...</td>\n",
|
| 881 |
-
" <td>0.00025</td>\n",
|
| 882 |
-
" </tr>\n",
|
| 883 |
-
" <tr>\n",
|
| 884 |
-
" <th>2</th>\n",
|
| 885 |
-
" <td>O[C@@H]1[C@@H](O)[C@@H](Cc2ccccc2)N(CC2CC2)C(=...</td>\n",
|
| 886 |
-
" <td>None</td>\n",
|
| 887 |
-
" <td>None</td>\n",
|
| 888 |
-
" <td>0.41</td>\n",
|
| 889 |
-
" <td>None</td>\n",
|
| 890 |
-
" <td>None</td>\n",
|
| 891 |
-
" <td>PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKM...</td>\n",
|
| 892 |
-
" <td>0.00041</td>\n",
|
| 893 |
-
" </tr>\n",
|
| 894 |
-
" <tr>\n",
|
| 895 |
-
" <th>3</th>\n",
|
| 896 |
-
" <td>OCCCCCCN1[C@H](Cc2ccccc2)[C@H](O)[C@@H](O)[C@@...</td>\n",
|
| 897 |
-
" <td>None</td>\n",
|
| 898 |
-
" <td>None</td>\n",
|
| 899 |
-
" <td>0.8</td>\n",
|
| 900 |
-
" <td>None</td>\n",
|
| 901 |
-
" <td>None</td>\n",
|
| 902 |
-
" <td>PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKM...</td>\n",
|
| 903 |
-
" <td>0.00080</td>\n",
|
| 904 |
-
" </tr>\n",
|
| 905 |
-
" <tr>\n",
|
| 906 |
-
" <th>4</th>\n",
|
| 907 |
-
" <td>OCCCCCN1[C@H](Cc2ccccc2)[C@H](O)[C@@H](O)[C@@H...</td>\n",
|
| 908 |
-
" <td>None</td>\n",
|
| 909 |
-
" <td>None</td>\n",
|
| 910 |
-
" <td>0.99</td>\n",
|
| 911 |
-
" <td>None</td>\n",
|
| 912 |
-
" <td>None</td>\n",
|
| 913 |
-
" <td>PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKM...</td>\n",
|
| 914 |
-
" <td>0.00099</td>\n",
|
| 915 |
-
" </tr>\n",
|
| 916 |
-
" <tr>\n",
|
| 917 |
-
" <th>...</th>\n",
|
| 918 |
-
" <td>...</td>\n",
|
| 919 |
-
" <td>...</td>\n",
|
| 920 |
-
" <td>...</td>\n",
|
| 921 |
-
" <td>...</td>\n",
|
| 922 |
-
" <td>...</td>\n",
|
| 923 |
-
" <td>...</td>\n",
|
| 924 |
-
" <td>...</td>\n",
|
| 925 |
-
" <td>...</td>\n",
|
| 926 |
-
" </tr>\n",
|
| 927 |
-
" <tr>\n",
|
| 928 |
-
" <th>5112</th>\n",
|
| 929 |
-
" <td>COc1ccc(NC(=O)N2CCC(CC2)C(=O)N[C@@H](CC(C)C)C(...</td>\n",
|
| 930 |
-
" <td>17</td>\n",
|
| 931 |
-
" <td>None</td>\n",
|
| 932 |
-
" <td>None</td>\n",
|
| 933 |
-
" <td>None</td>\n",
|
| 934 |
-
" <td>None</td>\n",
|
| 935 |
-
" <td>MSYDRAITVFSPDGHLFQVEYAQEAVKKGSTAVGVRGRDIVVLGVE...</td>\n",
|
| 936 |
-
" <td>0.01700</td>\n",
|
| 937 |
-
" </tr>\n",
|
| 938 |
-
" <tr>\n",
|
| 939 |
-
" <th>5113</th>\n",
|
| 940 |
-
" <td>CC(C)C[C@H](NC(=O)C1CCN(CC1)C(=O)Nc1cnccn1)C(=...</td>\n",
|
| 941 |
-
" <td>76</td>\n",
|
| 942 |
-
" <td>None</td>\n",
|
| 943 |
-
" <td>None</td>\n",
|
| 944 |
-
" <td>None</td>\n",
|
| 945 |
-
" <td>None</td>\n",
|
| 946 |
-
" <td>MSYDRAITVFSPDGHLFQVEYAQEAVKKGSTAVGVRGRDIVVLGVE...</td>\n",
|
| 947 |
-
" <td>0.07600</td>\n",
|
| 948 |
-
" </tr>\n",
|
| 949 |
-
" <tr>\n",
|
| 950 |
-
" <th>5313</th>\n",
|
| 951 |
-
" <td>C[C@H](NC(=O)[C@H](Cc1ccccc1)NC(=O)OCc1ccccc1)...</td>\n",
|
| 952 |
-
" <td>>100000</td>\n",
|
| 953 |
-
" <td>None</td>\n",
|
| 954 |
-
" <td>None</td>\n",
|
| 955 |
-
" <td>None</td>\n",
|
| 956 |
-
" <td>None</td>\n",
|
| 957 |
-
" <td>MSYDRAITVFSPDGHLFQVEYAQEAVKKGSTAVGVRGRDIVVLGVE...</td>\n",
|
| 958 |
-
" <td>100.00000</td>\n",
|
| 959 |
-
" </tr>\n",
|
| 960 |
-
" <tr>\n",
|
| 961 |
-
" <th>5314</th>\n",
|
| 962 |
-
" <td>FCC(=O)CNC(=O)[C@H](Cc1ccccc1)NC(=O)c1cccc2ccc...</td>\n",
|
| 963 |
-
" <td>>100000</td>\n",
|
| 964 |
-
" <td>None</td>\n",
|
| 965 |
-
" <td>None</td>\n",
|
| 966 |
-
" <td>None</td>\n",
|
| 967 |
-
" <td>None</td>\n",
|
| 968 |
-
" <td>MSYDRAITVFSPDGHLFQVEYAQEAVKKGSTAVGVRGRDIVVLGVE...</td>\n",
|
| 969 |
-
" <td>100.00000</td>\n",
|
| 970 |
-
" </tr>\n",
|
| 971 |
-
" <tr>\n",
|
| 972 |
-
" <th>5361</th>\n",
|
| 973 |
-
" <td>FCC(=O)CNC(=O)[C@H](Cc1ccccc1)NC(=O)c1ccccc1</td>\n",
|
| 974 |
-
" <td>>100000</td>\n",
|
| 975 |
-
" <td>None</td>\n",
|
| 976 |
-
" <td>None</td>\n",
|
| 977 |
-
" <td>None</td>\n",
|
| 978 |
-
" <td>None</td>\n",
|
| 979 |
-
" <td>MSYDRAITVFSPDGHLFQVEYAQEAVKKGSTAVGVRGRDIVVLGVE...</td>\n",
|
| 980 |
-
" <td>100.00000</td>\n",
|
| 981 |
-
" </tr>\n",
|
| 982 |
-
" </tbody>\n",
|
| 983 |
-
"</table>\n",
|
| 984 |
-
"<p>2510716 rows × 8 columns</p>\n",
|
| 985 |
-
"</div>"
|
| 986 |
-
],
|
| 987 |
-
"text/plain": [
|
| 988 |
-
" Ligand SMILES IC50 (nM) \\\n",
|
| 989 |
-
"0 COc1cc2c(Nc3ccc(Br)cc3F)ncnc2cc1OCC1CCN(C)CC1 None \n",
|
| 990 |
-
"1 O[C@@H]1[C@@H](O)[C@@H](Cc2ccccc2)N(C\\C=C\\c2cn... None \n",
|
| 991 |
-
"2 O[C@@H]1[C@@H](O)[C@@H](Cc2ccccc2)N(CC2CC2)C(=... None \n",
|
| 992 |
-
"3 OCCCCCCN1[C@H](Cc2ccccc2)[C@H](O)[C@@H](O)[C@@... None \n",
|
| 993 |
-
"4 OCCCCCN1[C@H](Cc2ccccc2)[C@H](O)[C@@H](O)[C@@H... None \n",
|
| 994 |
-
"... ... ... \n",
|
| 995 |
-
"5112 COc1ccc(NC(=O)N2CCC(CC2)C(=O)N[C@@H](CC(C)C)C(... 17 \n",
|
| 996 |
-
"5113 CC(C)C[C@H](NC(=O)C1CCN(CC1)C(=O)Nc1cnccn1)C(=... 76 \n",
|
| 997 |
-
"5313 C[C@H](NC(=O)[C@H](Cc1ccccc1)NC(=O)OCc1ccccc1)... >100000 \n",
|
| 998 |
-
"5314 FCC(=O)CNC(=O)[C@H](Cc1ccccc1)NC(=O)c1cccc2ccc... >100000 \n",
|
| 999 |
-
"5361 FCC(=O)CNC(=O)[C@H](Cc1ccccc1)NC(=O)c1ccccc1 >100000 \n",
|
| 1000 |
-
"\n",
|
| 1001 |
-
" KEGG ID of Ligand Ki (nM) Kd (nM) EC50 (nM) \\\n",
|
| 1002 |
-
"0 None 0.24 None None \n",
|
| 1003 |
-
"1 None 0.25 None None \n",
|
| 1004 |
-
"2 None 0.41 None None \n",
|
| 1005 |
-
"3 None 0.8 None None \n",
|
| 1006 |
-
"4 None 0.99 None None \n",
|
| 1007 |
-
"... ... ... ... ... \n",
|
| 1008 |
-
"5112 None None None None \n",
|
| 1009 |
-
"5113 None None None None \n",
|
| 1010 |
-
"5313 None None None None \n",
|
| 1011 |
-
"5314 None None None None \n",
|
| 1012 |
-
"5361 None None None None \n",
|
| 1013 |
-
"\n",
|
| 1014 |
-
" seq affinity_uM \n",
|
| 1015 |
-
"0 PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKM... 0.00024 \n",
|
| 1016 |
-
"1 PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKM... 0.00025 \n",
|
| 1017 |
-
"2 PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKM... 0.00041 \n",
|
| 1018 |
-
"3 PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKM... 0.00080 \n",
|
| 1019 |
-
"4 PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKM... 0.00099 \n",
|
| 1020 |
-
"... ... ... \n",
|
| 1021 |
-
"5112 MSYDRAITVFSPDGHLFQVEYAQEAVKKGSTAVGVRGRDIVVLGVE... 0.01700 \n",
|
| 1022 |
-
"5113 MSYDRAITVFSPDGHLFQVEYAQEAVKKGSTAVGVRGRDIVVLGVE... 0.07600 \n",
|
| 1023 |
-
"5313 MSYDRAITVFSPDGHLFQVEYAQEAVKKGSTAVGVRGRDIVVLGVE... 100.00000 \n",
|
| 1024 |
-
"5314 MSYDRAITVFSPDGHLFQVEYAQEAVKKGSTAVGVRGRDIVVLGVE... 100.00000 \n",
|
| 1025 |
-
"5361 MSYDRAITVFSPDGHLFQVEYAQEAVKKGSTAVGVRGRDIVVLGVE... 100.00000 \n",
|
| 1026 |
-
"\n",
|
| 1027 |
-
"[2510716 rows x 8 columns]"
|
| 1028 |
-
]
|
| 1029 |
-
},
|
| 1030 |
-
"execution_count": 6,
|
| 1031 |
-
"metadata": {},
|
| 1032 |
-
"output_type": "execute_result"
|
| 1033 |
-
}
|
| 1034 |
-
],
|
| 1035 |
-
"source": [
|
| 1036 |
-
"df_affinity"
|
| 1037 |
-
]
|
| 1038 |
-
},
|
| 1039 |
-
{
|
| 1040 |
-
"cell_type": "code",
|
| 1041 |
-
"execution_count": 25,
|
| 1042 |
-
"id": "20690729",
|
| 1043 |
-
"metadata": {},
|
| 1044 |
-
"outputs": [],
|
| 1045 |
-
"source": [
|
| 1046 |
-
"import rdkit.Chem as Chem"
|
| 1047 |
-
]
|
| 1048 |
-
},
|
| 1049 |
-
{
|
| 1050 |
-
"cell_type": "code",
|
| 1051 |
-
"execution_count": 27,
|
| 1052 |
-
"id": "48114dcc",
|
| 1053 |
-
"metadata": {},
|
| 1054 |
-
"outputs": [],
|
| 1055 |
-
"source": [
|
| 1056 |
-
"df_pdb = df[~df['PDB ID(s) for Ligand-Target Complex'].isnull()][['PDB ID(s) for Ligand-Target Complex','Ligand SMILES']]"
|
| 1057 |
-
]
|
| 1058 |
-
},
|
| 1059 |
-
{
|
| 1060 |
-
"cell_type": "code",
|
| 1061 |
-
"execution_count": 28,
|
| 1062 |
-
"id": "caa0497c",
|
| 1063 |
-
"metadata": {},
|
| 1064 |
-
"outputs": [],
|
| 1065 |
-
"source": [
|
| 1066 |
-
"def make_canonical(smi):\n",
|
| 1067 |
-
" return Chem.MolToSmiles(Chem.MolFromSmiles(smi))\n",
|
| 1068 |
-
"\n",
|
| 1069 |
-
"df_pdb['can_smiles'] = df_pdb['Ligand SMILES'].apply(make_canonical)"
|
| 1070 |
-
]
|
| 1071 |
-
},
|
| 1072 |
-
{
|
| 1073 |
-
"cell_type": "code",
|
| 1074 |
-
"execution_count": 29,
|
| 1075 |
-
"id": "e82d64f3",
|
| 1076 |
-
"metadata": {},
|
| 1077 |
-
"outputs": [
|
| 1078 |
-
{
|
| 1079 |
-
"data": {
|
| 1080 |
-
"text/html": [
|
| 1081 |
-
"<div>\n",
|
| 1082 |
-
"<style scoped>\n",
|
| 1083 |
-
" .dataframe tbody tr th:only-of-type {\n",
|
| 1084 |
-
" vertical-align: middle;\n",
|
| 1085 |
-
" }\n",
|
| 1086 |
-
"\n",
|
| 1087 |
-
" .dataframe tbody tr th {\n",
|
| 1088 |
-
" vertical-align: top;\n",
|
| 1089 |
-
" }\n",
|
| 1090 |
-
"\n",
|
| 1091 |
-
" .dataframe thead th {\n",
|
| 1092 |
-
" text-align: right;\n",
|
| 1093 |
-
" }\n",
|
| 1094 |
-
"</style>\n",
|
| 1095 |
-
"<table border=\"1\" class=\"dataframe\">\n",
|
| 1096 |
-
" <thead>\n",
|
| 1097 |
-
" <tr style=\"text-align: right;\">\n",
|
| 1098 |
-
" <th></th>\n",
|
| 1099 |
-
" <th>PDB ID(s) for Ligand-Target Complex</th>\n",
|
| 1100 |
-
" <th>Ligand SMILES</th>\n",
|
| 1101 |
-
" <th>can_smiles</th>\n",
|
| 1102 |
-
" </tr>\n",
|
| 1103 |
-
" </thead>\n",
|
| 1104 |
-
" <tbody>\n",
|
| 1105 |
-
" <tr>\n",
|
| 1106 |
-
" <th>0</th>\n",
|
| 1107 |
-
" <td>2IVU</td>\n",
|
| 1108 |
-
" <td>COc1cc2c(Nc3ccc(Br)cc3F)ncnc2cc1OCC1CCN(C)CC1</td>\n",
|
| 1109 |
-
" <td>COc1cc2c(Nc3ccc(Br)cc3F)ncnc2cc1OCC1CCN(C)CC1</td>\n",
|
| 1110 |
-
" </tr>\n",
|
| 1111 |
-
" <tr>\n",
|
| 1112 |
-
" <th>29</th>\n",
|
| 1113 |
-
" <td>1HWR</td>\n",
|
| 1114 |
-
" <td>O[C@@H]1[C@@H](O)[C@@H](Cc2ccccc2)N(CC=C)C(=O)...</td>\n",
|
| 1115 |
-
" <td>C=CCN1C(=O)N(CC=C)[C@H](Cc2ccccc2)[C@H](O)[C@@...</td>\n",
|
| 1116 |
-
" </tr>\n",
|
| 1117 |
-
" <tr>\n",
|
| 1118 |
-
" <th>34</th>\n",
|
| 1119 |
-
" <td>6DGY,6DH1,6DH4,6DH7,3O99</td>\n",
|
| 1120 |
-
" <td>CC[C@H](C)CN(C[C@@H](O)[C@H](Cc1ccccc1)NC(=O)O...</td>\n",
|
| 1121 |
-
" <td>CC[C@H](C)CN(C[C@@H](O)[C@H](Cc1ccccc1)NC(=O)O...</td>\n",
|
| 1122 |
-
" </tr>\n",
|
| 1123 |
-
" <tr>\n",
|
| 1124 |
-
" <th>129</th>\n",
|
| 1125 |
-
" <td>1MES,1MEU,1MET,1BVG,1BVE,3JVW,1QBS</td>\n",
|
| 1126 |
-
" <td>OCc1ccc(CN2[C@H](Cc3ccccc3)[C@H](O)[C@@H](O)[C...</td>\n",
|
| 1127 |
-
" <td>O=C1N(Cc2ccc(CO)cc2)[C@H](Cc2ccccc2)[C@H](O)[C...</td>\n",
|
| 1128 |
-
" </tr>\n",
|
| 1129 |
-
" <tr>\n",
|
| 1130 |
-
" <th>130</th>\n",
|
| 1131 |
-
" <td>1MER,1DMP,1RQ9</td>\n",
|
| 1132 |
-
" <td>Nc1cccc(CN2[C@H](Cc3ccccc3)[C@H](O)[C@@H](O)[C...</td>\n",
|
| 1133 |
-
" <td>Nc1cccc(CN2C(=O)N(Cc3cccc(N)c3)[C@H](Cc3ccccc3...</td>\n",
|
| 1134 |
-
" </tr>\n",
|
| 1135 |
-
" <tr>\n",
|
| 1136 |
-
" <th>...</th>\n",
|
| 1137 |
-
" <td>...</td>\n",
|
| 1138 |
-
" <td>...</td>\n",
|
| 1139 |
-
" <td>...</td>\n",
|
| 1140 |
-
" </tr>\n",
|
| 1141 |
-
" <tr>\n",
|
| 1142 |
-
" <th>2333375</th>\n",
|
| 1143 |
-
" <td>1MUI,2RKG,2RKF,1RV7,2Q5K,2O4S,6DJ1,6DJ2,3OGQ,2...</td>\n",
|
| 1144 |
-
" <td>CC(C)[C@H](N1CCCNC1=O)C(=O)N[C@H](C[C@H](O)[C@...</td>\n",
|
| 1145 |
-
" <td>Cc1cccc(C)c1OCC(=O)N[C@@H](Cc1ccccc1)[C@@H](O)...</td>\n",
|
| 1146 |
-
" </tr>\n",
|
| 1147 |
-
" <tr>\n",
|
| 1148 |
-
" <th>2333376</th>\n",
|
| 1149 |
-
" <td>4NPT,4DQH,4DQE,5E5J,3JW2,6OPU,6OPX,2HS2,2HS1,2...</td>\n",
|
| 1150 |
-
" <td>CC(C)CN(C[C@@H](O)[C@H](Cc1ccccc1)NC(=O)O[C@H]...</td>\n",
|
| 1151 |
-
" <td>CC(C)CN(C[C@@H](O)[C@H](Cc1ccccc1)NC(=O)O[C@H]...</td>\n",
|
| 1152 |
-
" </tr>\n",
|
| 1153 |
-
" <tr>\n",
|
| 1154 |
-
" <th>2333380</th>\n",
|
| 1155 |
-
" <td>6EKZ,1DY4,5FUK,6PS5</td>\n",
|
| 1156 |
-
" <td>CC(C)NCC(O)COc1cccc2ccccc12</td>\n",
|
| 1157 |
-
" <td>CC(C)NCC(O)COc1cccc2ccccc12</td>\n",
|
| 1158 |
-
" </tr>\n",
|
| 1159 |
-
" <tr>\n",
|
| 1160 |
-
" <th>2333384</th>\n",
|
| 1161 |
-
" <td>6EKZ,1DY4,5FUK,6PS5</td>\n",
|
| 1162 |
-
" <td>CC(C)NCC(O)COc1cccc2ccccc12</td>\n",
|
| 1163 |
-
" <td>CC(C)NCC(O)COc1cccc2ccccc12</td>\n",
|
| 1164 |
-
" </tr>\n",
|
| 1165 |
-
" <tr>\n",
|
| 1166 |
-
" <th>2333385</th>\n",
|
| 1167 |
-
" <td>6A60,3DCT</td>\n",
|
| 1168 |
-
" <td>CC(C)c1onc(c1COc1ccc(\\C=C\\c2cccc(c2)C(O)=O)c(C...</td>\n",
|
| 1169 |
-
" <td>CC(C)c1onc(-c2c(Cl)cccc2Cl)c1COc1ccc(/C=C/c2cc...</td>\n",
|
| 1170 |
-
" </tr>\n",
|
| 1171 |
-
" </tbody>\n",
|
| 1172 |
-
"</table>\n",
|
| 1173 |
-
"<p>123385 rows × 3 columns</p>\n",
|
| 1174 |
-
"</div>"
|
| 1175 |
-
],
|
| 1176 |
-
"text/plain": [
|
| 1177 |
-
" PDB ID(s) for Ligand-Target Complex \\\n",
|
| 1178 |
-
"0 2IVU \n",
|
| 1179 |
-
"29 1HWR \n",
|
| 1180 |
-
"34 6DGY,6DH1,6DH4,6DH7,3O99 \n",
|
| 1181 |
-
"129 1MES,1MEU,1MET,1BVG,1BVE,3JVW,1QBS \n",
|
| 1182 |
-
"130 1MER,1DMP,1RQ9 \n",
|
| 1183 |
-
"... ... \n",
|
| 1184 |
-
"2333375 1MUI,2RKG,2RKF,1RV7,2Q5K,2O4S,6DJ1,6DJ2,3OGQ,2... \n",
|
| 1185 |
-
"2333376 4NPT,4DQH,4DQE,5E5J,3JW2,6OPU,6OPX,2HS2,2HS1,2... \n",
|
| 1186 |
-
"2333380 6EKZ,1DY4,5FUK,6PS5 \n",
|
| 1187 |
-
"2333384 6EKZ,1DY4,5FUK,6PS5 \n",
|
| 1188 |
-
"2333385 6A60,3DCT \n",
|
| 1189 |
-
"\n",
|
| 1190 |
-
" Ligand SMILES \\\n",
|
| 1191 |
-
"0 COc1cc2c(Nc3ccc(Br)cc3F)ncnc2cc1OCC1CCN(C)CC1 \n",
|
| 1192 |
-
"29 O[C@@H]1[C@@H](O)[C@@H](Cc2ccccc2)N(CC=C)C(=O)... \n",
|
| 1193 |
-
"34 CC[C@H](C)CN(C[C@@H](O)[C@H](Cc1ccccc1)NC(=O)O... \n",
|
| 1194 |
-
"129 OCc1ccc(CN2[C@H](Cc3ccccc3)[C@H](O)[C@@H](O)[C... \n",
|
| 1195 |
-
"130 Nc1cccc(CN2[C@H](Cc3ccccc3)[C@H](O)[C@@H](O)[C... \n",
|
| 1196 |
-
"... ... \n",
|
| 1197 |
-
"2333375 CC(C)[C@H](N1CCCNC1=O)C(=O)N[C@H](C[C@H](O)[C@... \n",
|
| 1198 |
-
"2333376 CC(C)CN(C[C@@H](O)[C@H](Cc1ccccc1)NC(=O)O[C@H]... \n",
|
| 1199 |
-
"2333380 CC(C)NCC(O)COc1cccc2ccccc12 \n",
|
| 1200 |
-
"2333384 CC(C)NCC(O)COc1cccc2ccccc12 \n",
|
| 1201 |
-
"2333385 CC(C)c1onc(c1COc1ccc(\\C=C\\c2cccc(c2)C(O)=O)c(C... \n",
|
| 1202 |
-
"\n",
|
| 1203 |
-
" can_smiles \n",
|
| 1204 |
-
"0 COc1cc2c(Nc3ccc(Br)cc3F)ncnc2cc1OCC1CCN(C)CC1 \n",
|
| 1205 |
-
"29 C=CCN1C(=O)N(CC=C)[C@H](Cc2ccccc2)[C@H](O)[C@@... \n",
|
| 1206 |
-
"34 CC[C@H](C)CN(C[C@@H](O)[C@H](Cc1ccccc1)NC(=O)O... \n",
|
| 1207 |
-
"129 O=C1N(Cc2ccc(CO)cc2)[C@H](Cc2ccccc2)[C@H](O)[C... \n",
|
| 1208 |
-
"130 Nc1cccc(CN2C(=O)N(Cc3cccc(N)c3)[C@H](Cc3ccccc3... \n",
|
| 1209 |
-
"... ... \n",
|
| 1210 |
-
"2333375 Cc1cccc(C)c1OCC(=O)N[C@@H](Cc1ccccc1)[C@@H](O)... \n",
|
| 1211 |
-
"2333376 CC(C)CN(C[C@@H](O)[C@H](Cc1ccccc1)NC(=O)O[C@H]... \n",
|
| 1212 |
-
"2333380 CC(C)NCC(O)COc1cccc2ccccc12 \n",
|
| 1213 |
-
"2333384 CC(C)NCC(O)COc1cccc2ccccc12 \n",
|
| 1214 |
-
"2333385 CC(C)c1onc(-c2c(Cl)cccc2Cl)c1COc1ccc(/C=C/c2cc... \n",
|
| 1215 |
-
"\n",
|
| 1216 |
-
"[123385 rows x 3 columns]"
|
| 1217 |
-
]
|
| 1218 |
-
},
|
| 1219 |
-
"execution_count": 29,
|
| 1220 |
-
"metadata": {},
|
| 1221 |
-
"output_type": "execute_result"
|
| 1222 |
-
}
|
| 1223 |
-
],
|
| 1224 |
-
"source": [
|
| 1225 |
-
"df_pdb"
|
| 1226 |
-
]
|
| 1227 |
-
},
|
| 1228 |
-
{
|
| 1229 |
-
"cell_type": "code",
|
| 1230 |
-
"execution_count": null,
|
| 1231 |
-
"id": "593c9aec",
|
| 1232 |
-
"metadata": {},
|
| 1233 |
-
"outputs": [],
|
| 1234 |
-
"source": []
|
| 1235 |
-
}
|
| 1236 |
-
],
|
| 1237 |
-
"metadata": {
|
| 1238 |
-
"kernelspec": {
|
| 1239 |
-
"display_name": "Python 3",
|
| 1240 |
-
"language": "python",
|
| 1241 |
-
"name": "python3"
|
| 1242 |
-
},
|
| 1243 |
-
"language_info": {
|
| 1244 |
-
"codemirror_mode": {
|
| 1245 |
-
"name": "ipython",
|
| 1246 |
-
"version": 3
|
| 1247 |
-
},
|
| 1248 |
-
"file_extension": ".py",
|
| 1249 |
-
"mimetype": "text/x-python",
|
| 1250 |
-
"name": "python",
|
| 1251 |
-
"nbconvert_exporter": "python",
|
| 1252 |
-
"pygments_lexer": "ipython3",
|
| 1253 |
-
"version": "3.9.4"
|
| 1254 |
-
}
|
| 1255 |
-
},
|
| 1256 |
-
"nbformat": 4,
|
| 1257 |
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"nbformat_minor": 5
|
| 1258 |
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}
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|
bindingdb_single.ipynb
DELETED
|
@@ -1,1036 +0,0 @@
|
|
| 1 |
-
{
|
| 2 |
-
"cells": [
|
| 3 |
-
{
|
| 4 |
-
"cell_type": "code",
|
| 5 |
-
"execution_count": 1,
|
| 6 |
-
"id": "ecce356e-321b-441e-8a5d-a20bf72f8691",
|
| 7 |
-
"metadata": {},
|
| 8 |
-
"outputs": [],
|
| 9 |
-
"source": [
|
| 10 |
-
"import pandas as pd\n",
|
| 11 |
-
"import dask.dataframe as dd"
|
| 12 |
-
]
|
| 13 |
-
},
|
| 14 |
-
{
|
| 15 |
-
"cell_type": "code",
|
| 16 |
-
"execution_count": 2,
|
| 17 |
-
"id": "89cbcd82-4ca2-4aba-95b7-e58c0ceed770",
|
| 18 |
-
"metadata": {},
|
| 19 |
-
"outputs": [],
|
| 20 |
-
"source": [
|
| 21 |
-
"cols = ['Ligand SMILES', 'IC50 (nM)','KEGG ID of Ligand','Ki (nM)', 'Kd (nM)','EC50 (nM)']"
|
| 22 |
-
]
|
| 23 |
-
},
|
| 24 |
-
{
|
| 25 |
-
"cell_type": "code",
|
| 26 |
-
"execution_count": 3,
|
| 27 |
-
"id": "1b923f02-e858-4737-ab5e-4c98c2def1b6",
|
| 28 |
-
"metadata": {},
|
| 29 |
-
"outputs": [],
|
| 30 |
-
"source": [
|
| 31 |
-
"allseq = ['BindingDB Target Chain Sequence']+['BindingDB Target Chain Sequence.{}'.format(j) for j in range(1,13)]"
|
| 32 |
-
]
|
| 33 |
-
},
|
| 34 |
-
{
|
| 35 |
-
"cell_type": "code",
|
| 36 |
-
"execution_count": 4,
|
| 37 |
-
"id": "54f0721e-1e36-48cc-a635-a1617a04f9e5",
|
| 38 |
-
"metadata": {},
|
| 39 |
-
"outputs": [],
|
| 40 |
-
"source": [
|
| 41 |
-
"dtypes = {'BindingDB Target Chain Sequence.{}'.format(i): 'object' for i in range(1,13)}"
|
| 42 |
-
]
|
| 43 |
-
},
|
| 44 |
-
{
|
| 45 |
-
"cell_type": "code",
|
| 46 |
-
"execution_count": 5,
|
| 47 |
-
"id": "c1843c65-a5ef-4604-adca-40fb18bc2991",
|
| 48 |
-
"metadata": {},
|
| 49 |
-
"outputs": [],
|
| 50 |
-
"source": [
|
| 51 |
-
"dtypes.update({'BindingDB Target Chain Sequence': 'object',\n",
|
| 52 |
-
" 'IC50 (nM)': 'object',\n",
|
| 53 |
-
" 'KEGG ID of Ligand': 'object',\n",
|
| 54 |
-
" 'Ki (nM)': 'object',\n",
|
| 55 |
-
" 'Kd (nM)': 'object',\n",
|
| 56 |
-
" 'EC50 (nM)': 'object',\n",
|
| 57 |
-
" 'koff (s-1)': 'object'})"
|
| 58 |
-
]
|
| 59 |
-
},
|
| 60 |
-
{
|
| 61 |
-
"cell_type": "code",
|
| 62 |
-
"execution_count": 6,
|
| 63 |
-
"id": "aa337ac3-4ca1-4369-a9c7-ab705221a137",
|
| 64 |
-
"metadata": {},
|
| 65 |
-
"outputs": [],
|
| 66 |
-
"source": [
|
| 67 |
-
"seq_name = 'BindingDB Target Chain Sequence'"
|
| 68 |
-
]
|
| 69 |
-
},
|
| 70 |
-
{
|
| 71 |
-
"cell_type": "code",
|
| 72 |
-
"execution_count": 7,
|
| 73 |
-
"id": "a870d8d7-374b-4474-b9ee-305bbf9f17a9",
|
| 74 |
-
"metadata": {},
|
| 75 |
-
"outputs": [],
|
| 76 |
-
"source": [
|
| 77 |
-
"import tqdm.notebook"
|
| 78 |
-
]
|
| 79 |
-
},
|
| 80 |
-
{
|
| 81 |
-
"cell_type": "code",
|
| 82 |
-
"execution_count": 8,
|
| 83 |
-
"id": "e97d29e6-d153-480e-a1ee-c216c22af8d2",
|
| 84 |
-
"metadata": {},
|
| 85 |
-
"outputs": [],
|
| 86 |
-
"source": [
|
| 87 |
-
"ddf = dd.read_csv('bindingdb/data/BindingDB_All.tsv',sep='\\t',\n",
|
| 88 |
-
" error_bad_lines=False,\n",
|
| 89 |
-
" usecols=cols+allseq,\n",
|
| 90 |
-
" dtype=dtypes)"
|
| 91 |
-
]
|
| 92 |
-
},
|
| 93 |
-
{
|
| 94 |
-
"cell_type": "code",
|
| 95 |
-
"execution_count": 11,
|
| 96 |
-
"id": "a4b381c0-968b-4248-a24f-9609acb12136",
|
| 97 |
-
"metadata": {},
|
| 98 |
-
"outputs": [
|
| 99 |
-
{
|
| 100 |
-
"data": {
|
| 101 |
-
"text/plain": [
|
| 102 |
-
"0.04789522637942933"
|
| 103 |
-
]
|
| 104 |
-
},
|
| 105 |
-
"execution_count": 11,
|
| 106 |
-
"metadata": {},
|
| 107 |
-
"output_type": "execute_result"
|
| 108 |
-
}
|
| 109 |
-
],
|
| 110 |
-
"source": [
|
| 111 |
-
"len(ddf[~ddf['BindingDB Target Chain Sequence.1'].isnull()])/len(ddf[~ddf['BindingDB Target Chain Sequence'].isnull()])"
|
| 112 |
-
]
|
| 113 |
-
},
|
| 114 |
-
{
|
| 115 |
-
"cell_type": "code",
|
| 116 |
-
"execution_count": 18,
|
| 117 |
-
"id": "7a8efd3f-c9c4-4f6a-83e5-9e9610deb17f",
|
| 118 |
-
"metadata": {},
|
| 119 |
-
"outputs": [],
|
| 120 |
-
"source": [
|
| 121 |
-
"# remove rows with more than one target\n",
|
| 122 |
-
"ddf_prune = ddf[ddf['BindingDB Target Chain Sequence.1'].isnull()]\n",
|
| 123 |
-
"ddf_prune = ddf_prune.rename(columns={'BindingDB Target Chain Sequence': 'seq'})"
|
| 124 |
-
]
|
| 125 |
-
},
|
| 126 |
-
{
|
| 127 |
-
"cell_type": "code",
|
| 128 |
-
"execution_count": 19,
|
| 129 |
-
"id": "c00102b8-f4be-4ebd-8d30-7a2c7fc2d05e",
|
| 130 |
-
"metadata": {},
|
| 131 |
-
"outputs": [],
|
| 132 |
-
"source": [
|
| 133 |
-
"ddf_nonnull = ddf_prune[~ddf_prune.seq.isnull()].copy()"
|
| 134 |
-
]
|
| 135 |
-
},
|
| 136 |
-
{
|
| 137 |
-
"cell_type": "code",
|
| 138 |
-
"execution_count": 20,
|
| 139 |
-
"id": "c5337e06-1e45-4180-90ed-49ac9ecdd24a",
|
| 140 |
-
"metadata": {},
|
| 141 |
-
"outputs": [
|
| 142 |
-
{
|
| 143 |
-
"data": {
|
| 144 |
-
"text/html": [
|
| 145 |
-
"<div>\n",
|
| 146 |
-
"<style scoped>\n",
|
| 147 |
-
" .dataframe tbody tr th:only-of-type {\n",
|
| 148 |
-
" vertical-align: middle;\n",
|
| 149 |
-
" }\n",
|
| 150 |
-
"\n",
|
| 151 |
-
" .dataframe tbody tr th {\n",
|
| 152 |
-
" vertical-align: top;\n",
|
| 153 |
-
" }\n",
|
| 154 |
-
"\n",
|
| 155 |
-
" .dataframe thead th {\n",
|
| 156 |
-
" text-align: right;\n",
|
| 157 |
-
" }\n",
|
| 158 |
-
"</style>\n",
|
| 159 |
-
"<table border=\"1\" class=\"dataframe\">\n",
|
| 160 |
-
" <thead>\n",
|
| 161 |
-
" <tr style=\"text-align: right;\">\n",
|
| 162 |
-
" <th></th>\n",
|
| 163 |
-
" <th>Ligand SMILES</th>\n",
|
| 164 |
-
" <th>Ki (nM)</th>\n",
|
| 165 |
-
" <th>IC50 (nM)</th>\n",
|
| 166 |
-
" <th>Kd (nM)</th>\n",
|
| 167 |
-
" <th>EC50 (nM)</th>\n",
|
| 168 |
-
" <th>KEGG ID of Ligand</th>\n",
|
| 169 |
-
" <th>seq</th>\n",
|
| 170 |
-
" <th>BindingDB Target Chain Sequence.1</th>\n",
|
| 171 |
-
" <th>BindingDB Target Chain Sequence.2</th>\n",
|
| 172 |
-
" <th>BindingDB Target Chain Sequence.3</th>\n",
|
| 173 |
-
" <th>BindingDB Target Chain Sequence.4</th>\n",
|
| 174 |
-
" <th>BindingDB Target Chain Sequence.5</th>\n",
|
| 175 |
-
" <th>BindingDB Target Chain Sequence.6</th>\n",
|
| 176 |
-
" <th>BindingDB Target Chain Sequence.7</th>\n",
|
| 177 |
-
" <th>BindingDB Target Chain Sequence.8</th>\n",
|
| 178 |
-
" <th>BindingDB Target Chain Sequence.9</th>\n",
|
| 179 |
-
" <th>BindingDB Target Chain Sequence.10</th>\n",
|
| 180 |
-
" <th>BindingDB Target Chain Sequence.11</th>\n",
|
| 181 |
-
" <th>BindingDB Target Chain Sequence.12</th>\n",
|
| 182 |
-
" </tr>\n",
|
| 183 |
-
" </thead>\n",
|
| 184 |
-
" <tbody>\n",
|
| 185 |
-
" <tr>\n",
|
| 186 |
-
" <th>0</th>\n",
|
| 187 |
-
" <td>COc1cc2c(Nc3ccc(Br)cc3F)ncnc2cc1OCC1CCN(C)CC1</td>\n",
|
| 188 |
-
" <td>0.24</td>\n",
|
| 189 |
-
" <td>NaN</td>\n",
|
| 190 |
-
" <td>NaN</td>\n",
|
| 191 |
-
" <td>NaN</td>\n",
|
| 192 |
-
" <td>NaN</td>\n",
|
| 193 |
-
" <td>PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKM...</td>\n",
|
| 194 |
-
" <td>NaN</td>\n",
|
| 195 |
-
" <td>NaN</td>\n",
|
| 196 |
-
" <td>NaN</td>\n",
|
| 197 |
-
" <td>NaN</td>\n",
|
| 198 |
-
" <td>NaN</td>\n",
|
| 199 |
-
" <td>NaN</td>\n",
|
| 200 |
-
" <td>NaN</td>\n",
|
| 201 |
-
" <td>NaN</td>\n",
|
| 202 |
-
" <td>NaN</td>\n",
|
| 203 |
-
" <td>NaN</td>\n",
|
| 204 |
-
" <td>NaN</td>\n",
|
| 205 |
-
" <td>NaN</td>\n",
|
| 206 |
-
" </tr>\n",
|
| 207 |
-
" <tr>\n",
|
| 208 |
-
" <th>1</th>\n",
|
| 209 |
-
" <td>O[C@@H]1[C@@H](O)[C@@H](Cc2ccccc2)N(C\\C=C\\c2cn...</td>\n",
|
| 210 |
-
" <td>0.25</td>\n",
|
| 211 |
-
" <td>NaN</td>\n",
|
| 212 |
-
" <td>NaN</td>\n",
|
| 213 |
-
" <td>NaN</td>\n",
|
| 214 |
-
" <td>NaN</td>\n",
|
| 215 |
-
" <td>PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKM...</td>\n",
|
| 216 |
-
" <td>NaN</td>\n",
|
| 217 |
-
" <td>NaN</td>\n",
|
| 218 |
-
" <td>NaN</td>\n",
|
| 219 |
-
" <td>NaN</td>\n",
|
| 220 |
-
" <td>NaN</td>\n",
|
| 221 |
-
" <td>NaN</td>\n",
|
| 222 |
-
" <td>NaN</td>\n",
|
| 223 |
-
" <td>NaN</td>\n",
|
| 224 |
-
" <td>NaN</td>\n",
|
| 225 |
-
" <td>NaN</td>\n",
|
| 226 |
-
" <td>NaN</td>\n",
|
| 227 |
-
" <td>NaN</td>\n",
|
| 228 |
-
" </tr>\n",
|
| 229 |
-
" <tr>\n",
|
| 230 |
-
" <th>2</th>\n",
|
| 231 |
-
" <td>O[C@@H]1[C@@H](O)[C@@H](Cc2ccccc2)N(CC2CC2)C(=...</td>\n",
|
| 232 |
-
" <td>0.41</td>\n",
|
| 233 |
-
" <td>NaN</td>\n",
|
| 234 |
-
" <td>NaN</td>\n",
|
| 235 |
-
" <td>NaN</td>\n",
|
| 236 |
-
" <td>NaN</td>\n",
|
| 237 |
-
" <td>PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKM...</td>\n",
|
| 238 |
-
" <td>NaN</td>\n",
|
| 239 |
-
" <td>NaN</td>\n",
|
| 240 |
-
" <td>NaN</td>\n",
|
| 241 |
-
" <td>NaN</td>\n",
|
| 242 |
-
" <td>NaN</td>\n",
|
| 243 |
-
" <td>NaN</td>\n",
|
| 244 |
-
" <td>NaN</td>\n",
|
| 245 |
-
" <td>NaN</td>\n",
|
| 246 |
-
" <td>NaN</td>\n",
|
| 247 |
-
" <td>NaN</td>\n",
|
| 248 |
-
" <td>NaN</td>\n",
|
| 249 |
-
" <td>NaN</td>\n",
|
| 250 |
-
" </tr>\n",
|
| 251 |
-
" <tr>\n",
|
| 252 |
-
" <th>3</th>\n",
|
| 253 |
-
" <td>OCCCCCCN1[C@H](Cc2ccccc2)[C@H](O)[C@@H](O)[C@@...</td>\n",
|
| 254 |
-
" <td>0.8</td>\n",
|
| 255 |
-
" <td>NaN</td>\n",
|
| 256 |
-
" <td>NaN</td>\n",
|
| 257 |
-
" <td>NaN</td>\n",
|
| 258 |
-
" <td>NaN</td>\n",
|
| 259 |
-
" <td>PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKM...</td>\n",
|
| 260 |
-
" <td>NaN</td>\n",
|
| 261 |
-
" <td>NaN</td>\n",
|
| 262 |
-
" <td>NaN</td>\n",
|
| 263 |
-
" <td>NaN</td>\n",
|
| 264 |
-
" <td>NaN</td>\n",
|
| 265 |
-
" <td>NaN</td>\n",
|
| 266 |
-
" <td>NaN</td>\n",
|
| 267 |
-
" <td>NaN</td>\n",
|
| 268 |
-
" <td>NaN</td>\n",
|
| 269 |
-
" <td>NaN</td>\n",
|
| 270 |
-
" <td>NaN</td>\n",
|
| 271 |
-
" <td>NaN</td>\n",
|
| 272 |
-
" </tr>\n",
|
| 273 |
-
" <tr>\n",
|
| 274 |
-
" <th>4</th>\n",
|
| 275 |
-
" <td>OCCCCCN1[C@H](Cc2ccccc2)[C@H](O)[C@@H](O)[C@@H...</td>\n",
|
| 276 |
-
" <td>0.99</td>\n",
|
| 277 |
-
" <td>NaN</td>\n",
|
| 278 |
-
" <td>NaN</td>\n",
|
| 279 |
-
" <td>NaN</td>\n",
|
| 280 |
-
" <td>NaN</td>\n",
|
| 281 |
-
" <td>PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKM...</td>\n",
|
| 282 |
-
" <td>NaN</td>\n",
|
| 283 |
-
" <td>NaN</td>\n",
|
| 284 |
-
" <td>NaN</td>\n",
|
| 285 |
-
" <td>NaN</td>\n",
|
| 286 |
-
" <td>NaN</td>\n",
|
| 287 |
-
" <td>NaN</td>\n",
|
| 288 |
-
" <td>NaN</td>\n",
|
| 289 |
-
" <td>NaN</td>\n",
|
| 290 |
-
" <td>NaN</td>\n",
|
| 291 |
-
" <td>NaN</td>\n",
|
| 292 |
-
" <td>NaN</td>\n",
|
| 293 |
-
" <td>NaN</td>\n",
|
| 294 |
-
" </tr>\n",
|
| 295 |
-
" </tbody>\n",
|
| 296 |
-
"</table>\n",
|
| 297 |
-
"</div>"
|
| 298 |
-
],
|
| 299 |
-
"text/plain": [
|
| 300 |
-
" Ligand SMILES Ki (nM) IC50 (nM) \\\n",
|
| 301 |
-
"0 COc1cc2c(Nc3ccc(Br)cc3F)ncnc2cc1OCC1CCN(C)CC1 0.24 NaN \n",
|
| 302 |
-
"1 O[C@@H]1[C@@H](O)[C@@H](Cc2ccccc2)N(C\\C=C\\c2cn... 0.25 NaN \n",
|
| 303 |
-
"2 O[C@@H]1[C@@H](O)[C@@H](Cc2ccccc2)N(CC2CC2)C(=... 0.41 NaN \n",
|
| 304 |
-
"3 OCCCCCCN1[C@H](Cc2ccccc2)[C@H](O)[C@@H](O)[C@@... 0.8 NaN \n",
|
| 305 |
-
"4 OCCCCCN1[C@H](Cc2ccccc2)[C@H](O)[C@@H](O)[C@@H... 0.99 NaN \n",
|
| 306 |
-
"\n",
|
| 307 |
-
" Kd (nM) EC50 (nM) KEGG ID of Ligand \\\n",
|
| 308 |
-
"0 NaN NaN NaN \n",
|
| 309 |
-
"1 NaN NaN NaN \n",
|
| 310 |
-
"2 NaN NaN NaN \n",
|
| 311 |
-
"3 NaN NaN NaN \n",
|
| 312 |
-
"4 NaN NaN NaN \n",
|
| 313 |
-
"\n",
|
| 314 |
-
" seq \\\n",
|
| 315 |
-
"0 PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKM... \n",
|
| 316 |
-
"1 PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKM... \n",
|
| 317 |
-
"2 PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKM... \n",
|
| 318 |
-
"3 PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKM... \n",
|
| 319 |
-
"4 PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKM... \n",
|
| 320 |
-
"\n",
|
| 321 |
-
" BindingDB Target Chain Sequence.1 BindingDB Target Chain Sequence.2 \\\n",
|
| 322 |
-
"0 NaN NaN \n",
|
| 323 |
-
"1 NaN NaN \n",
|
| 324 |
-
"2 NaN NaN \n",
|
| 325 |
-
"3 NaN NaN \n",
|
| 326 |
-
"4 NaN NaN \n",
|
| 327 |
-
"\n",
|
| 328 |
-
" BindingDB Target Chain Sequence.3 BindingDB Target Chain Sequence.4 \\\n",
|
| 329 |
-
"0 NaN NaN \n",
|
| 330 |
-
"1 NaN NaN \n",
|
| 331 |
-
"2 NaN NaN \n",
|
| 332 |
-
"3 NaN NaN \n",
|
| 333 |
-
"4 NaN NaN \n",
|
| 334 |
-
"\n",
|
| 335 |
-
" BindingDB Target Chain Sequence.5 BindingDB Target Chain Sequence.6 \\\n",
|
| 336 |
-
"0 NaN NaN \n",
|
| 337 |
-
"1 NaN NaN \n",
|
| 338 |
-
"2 NaN NaN \n",
|
| 339 |
-
"3 NaN NaN \n",
|
| 340 |
-
"4 NaN NaN \n",
|
| 341 |
-
"\n",
|
| 342 |
-
" BindingDB Target Chain Sequence.7 BindingDB Target Chain Sequence.8 \\\n",
|
| 343 |
-
"0 NaN NaN \n",
|
| 344 |
-
"1 NaN NaN \n",
|
| 345 |
-
"2 NaN NaN \n",
|
| 346 |
-
"3 NaN NaN \n",
|
| 347 |
-
"4 NaN NaN \n",
|
| 348 |
-
"\n",
|
| 349 |
-
" BindingDB Target Chain Sequence.9 BindingDB Target Chain Sequence.10 \\\n",
|
| 350 |
-
"0 NaN NaN \n",
|
| 351 |
-
"1 NaN NaN \n",
|
| 352 |
-
"2 NaN NaN \n",
|
| 353 |
-
"3 NaN NaN \n",
|
| 354 |
-
"4 NaN NaN \n",
|
| 355 |
-
"\n",
|
| 356 |
-
" BindingDB Target Chain Sequence.11 BindingDB Target Chain Sequence.12 \n",
|
| 357 |
-
"0 NaN NaN \n",
|
| 358 |
-
"1 NaN NaN \n",
|
| 359 |
-
"2 NaN NaN \n",
|
| 360 |
-
"3 NaN NaN \n",
|
| 361 |
-
"4 NaN NaN "
|
| 362 |
-
]
|
| 363 |
-
},
|
| 364 |
-
"execution_count": 20,
|
| 365 |
-
"metadata": {},
|
| 366 |
-
"output_type": "execute_result"
|
| 367 |
-
}
|
| 368 |
-
],
|
| 369 |
-
"source": [
|
| 370 |
-
"ddf_nonnull.head()"
|
| 371 |
-
]
|
| 372 |
-
},
|
| 373 |
-
{
|
| 374 |
-
"cell_type": "code",
|
| 375 |
-
"execution_count": 21,
|
| 376 |
-
"id": "7b423365-4989-4325-a5a5-845d852d52e9",
|
| 377 |
-
"metadata": {},
|
| 378 |
-
"outputs": [
|
| 379 |
-
{
|
| 380 |
-
"data": {
|
| 381 |
-
"text/plain": [
|
| 382 |
-
"2221761"
|
| 383 |
-
]
|
| 384 |
-
},
|
| 385 |
-
"execution_count": 21,
|
| 386 |
-
"metadata": {},
|
| 387 |
-
"output_type": "execute_result"
|
| 388 |
-
}
|
| 389 |
-
],
|
| 390 |
-
"source": [
|
| 391 |
-
"len(ddf_nonnull)"
|
| 392 |
-
]
|
| 393 |
-
},
|
| 394 |
-
{
|
| 395 |
-
"cell_type": "code",
|
| 396 |
-
"execution_count": 22,
|
| 397 |
-
"id": "872edb84-3459-43d9-8e0e-e2a6b5d281eb",
|
| 398 |
-
"metadata": {},
|
| 399 |
-
"outputs": [],
|
| 400 |
-
"source": [
|
| 401 |
-
"from pint import UnitRegistry\n",
|
| 402 |
-
"import numpy as np\n",
|
| 403 |
-
"import re\n",
|
| 404 |
-
"ureg = UnitRegistry()\n",
|
| 405 |
-
"\n",
|
| 406 |
-
"def to_uM(affinities):\n",
|
| 407 |
-
" ic50, Ki, Kd, ec50 = affinities\n",
|
| 408 |
-
"\n",
|
| 409 |
-
" vals = []\n",
|
| 410 |
-
" \n",
|
| 411 |
-
" try:\n",
|
| 412 |
-
" ic50 = ureg(str(ic50)+'nM').m_as(ureg.uM)\n",
|
| 413 |
-
" vals.append(ic50)\n",
|
| 414 |
-
" except:\n",
|
| 415 |
-
" pass\n",
|
| 416 |
-
"\n",
|
| 417 |
-
" try:\n",
|
| 418 |
-
" Ki = ureg(str(Ki)+'nM').m_as(ureg.uM)\n",
|
| 419 |
-
" vals.append(Ki)\n",
|
| 420 |
-
" except:\n",
|
| 421 |
-
" pass\n",
|
| 422 |
-
"\n",
|
| 423 |
-
" try:\n",
|
| 424 |
-
" Kd = ureg(str(Kd)+'nM').m_as(ureg.uM)\n",
|
| 425 |
-
" vals.append(Kd)\n",
|
| 426 |
-
" except:\n",
|
| 427 |
-
" pass\n",
|
| 428 |
-
"\n",
|
| 429 |
-
" try:\n",
|
| 430 |
-
" ec50 = ureg(str(ec50)+'nM').m_as(ureg.uM)\n",
|
| 431 |
-
" vals.append(ec50)\n",
|
| 432 |
-
" except:\n",
|
| 433 |
-
" pass\n",
|
| 434 |
-
"\n",
|
| 435 |
-
" if len(vals) > 0:\n",
|
| 436 |
-
" vals = np.array(vals)\n",
|
| 437 |
-
" return np.mean(vals[~np.isnan(vals)])\n",
|
| 438 |
-
" \n",
|
| 439 |
-
" return None"
|
| 440 |
-
]
|
| 441 |
-
},
|
| 442 |
-
{
|
| 443 |
-
"cell_type": "code",
|
| 444 |
-
"execution_count": 23,
|
| 445 |
-
"id": "b3cff13c-19b2-4413-a84b-d99062f516a7",
|
| 446 |
-
"metadata": {},
|
| 447 |
-
"outputs": [],
|
| 448 |
-
"source": [
|
| 449 |
-
"df_nonnull = ddf_nonnull.compute()"
|
| 450 |
-
]
|
| 451 |
-
},
|
| 452 |
-
{
|
| 453 |
-
"cell_type": "code",
|
| 454 |
-
"execution_count": 24,
|
| 455 |
-
"id": "ca9795de-e821-4dc3-a7bf-70ade9e4c7f0",
|
| 456 |
-
"metadata": {},
|
| 457 |
-
"outputs": [
|
| 458 |
-
{
|
| 459 |
-
"name": "stdout",
|
| 460 |
-
"output_type": "stream",
|
| 461 |
-
"text": [
|
| 462 |
-
"INFO: Pandarallel will run on 32 workers.\n",
|
| 463 |
-
"INFO: Pandarallel will use Memory file system to transfer data between the main process and workers.\n"
|
| 464 |
-
]
|
| 465 |
-
}
|
| 466 |
-
],
|
| 467 |
-
"source": [
|
| 468 |
-
"from pandarallel import pandarallel\n",
|
| 469 |
-
"pandarallel.initialize()\n"
|
| 470 |
-
]
|
| 471 |
-
},
|
| 472 |
-
{
|
| 473 |
-
"cell_type": "code",
|
| 474 |
-
"execution_count": 25,
|
| 475 |
-
"id": "4356a3e2-fede-48e7-a486-343661fe0a0a",
|
| 476 |
-
"metadata": {},
|
| 477 |
-
"outputs": [],
|
| 478 |
-
"source": [
|
| 479 |
-
"df_affinity = df_nonnull.copy()\n",
|
| 480 |
-
"df_affinity['affinity_uM'] = df_affinity[['IC50 (nM)', 'Ki (nM)', 'Kd (nM)','EC50 (nM)']].parallel_apply(to_uM,axis=1)"
|
| 481 |
-
]
|
| 482 |
-
},
|
| 483 |
-
{
|
| 484 |
-
"cell_type": "code",
|
| 485 |
-
"execution_count": 26,
|
| 486 |
-
"id": "e91c3af8-84a5-42a2-9e25-49cb2f320b0b",
|
| 487 |
-
"metadata": {},
|
| 488 |
-
"outputs": [],
|
| 489 |
-
"source": [
|
| 490 |
-
"df_affinity[~df_affinity['affinity_uM'].isnull()].to_parquet('data/bindingdb.parquet')"
|
| 491 |
-
]
|
| 492 |
-
},
|
| 493 |
-
{
|
| 494 |
-
"cell_type": "code",
|
| 495 |
-
"execution_count": 27,
|
| 496 |
-
"id": "f3a9173e-d574-4314-9cea-f8c0a66766c0",
|
| 497 |
-
"metadata": {},
|
| 498 |
-
"outputs": [],
|
| 499 |
-
"source": [
|
| 500 |
-
"import pandas as pd\n",
|
| 501 |
-
"df_affinity = pd.read_parquet('data/bindingdb.parquet')"
|
| 502 |
-
]
|
| 503 |
-
},
|
| 504 |
-
{
|
| 505 |
-
"cell_type": "code",
|
| 506 |
-
"execution_count": 28,
|
| 507 |
-
"id": "f602fdbe-7083-436c-9eac-9d97fbc8be67",
|
| 508 |
-
"metadata": {},
|
| 509 |
-
"outputs": [
|
| 510 |
-
{
|
| 511 |
-
"data": {
|
| 512 |
-
"text/plain": [
|
| 513 |
-
"2219812"
|
| 514 |
-
]
|
| 515 |
-
},
|
| 516 |
-
"execution_count": 28,
|
| 517 |
-
"metadata": {},
|
| 518 |
-
"output_type": "execute_result"
|
| 519 |
-
}
|
| 520 |
-
],
|
| 521 |
-
"source": [
|
| 522 |
-
"len(df_affinity)"
|
| 523 |
-
]
|
| 524 |
-
},
|
| 525 |
-
{
|
| 526 |
-
"cell_type": "code",
|
| 527 |
-
"execution_count": 29,
|
| 528 |
-
"id": "27194288-cf3e-4c30-ad55-3b0998fdf939",
|
| 529 |
-
"metadata": {},
|
| 530 |
-
"outputs": [
|
| 531 |
-
{
|
| 532 |
-
"data": {
|
| 533 |
-
"text/html": [
|
| 534 |
-
"<div>\n",
|
| 535 |
-
"<style scoped>\n",
|
| 536 |
-
" .dataframe tbody tr th:only-of-type {\n",
|
| 537 |
-
" vertical-align: middle;\n",
|
| 538 |
-
" }\n",
|
| 539 |
-
"\n",
|
| 540 |
-
" .dataframe tbody tr th {\n",
|
| 541 |
-
" vertical-align: top;\n",
|
| 542 |
-
" }\n",
|
| 543 |
-
"\n",
|
| 544 |
-
" .dataframe thead th {\n",
|
| 545 |
-
" text-align: right;\n",
|
| 546 |
-
" }\n",
|
| 547 |
-
"</style>\n",
|
| 548 |
-
"<table border=\"1\" class=\"dataframe\">\n",
|
| 549 |
-
" <thead>\n",
|
| 550 |
-
" <tr style=\"text-align: right;\">\n",
|
| 551 |
-
" <th></th>\n",
|
| 552 |
-
" <th>Ligand SMILES</th>\n",
|
| 553 |
-
" <th>Ki (nM)</th>\n",
|
| 554 |
-
" <th>IC50 (nM)</th>\n",
|
| 555 |
-
" <th>Kd (nM)</th>\n",
|
| 556 |
-
" <th>EC50 (nM)</th>\n",
|
| 557 |
-
" <th>KEGG ID of Ligand</th>\n",
|
| 558 |
-
" <th>seq</th>\n",
|
| 559 |
-
" <th>BindingDB Target Chain Sequence.1</th>\n",
|
| 560 |
-
" <th>BindingDB Target Chain Sequence.2</th>\n",
|
| 561 |
-
" <th>BindingDB Target Chain Sequence.3</th>\n",
|
| 562 |
-
" <th>BindingDB Target Chain Sequence.4</th>\n",
|
| 563 |
-
" <th>BindingDB Target Chain Sequence.5</th>\n",
|
| 564 |
-
" <th>BindingDB Target Chain Sequence.6</th>\n",
|
| 565 |
-
" <th>BindingDB Target Chain Sequence.7</th>\n",
|
| 566 |
-
" <th>BindingDB Target Chain Sequence.8</th>\n",
|
| 567 |
-
" <th>BindingDB Target Chain Sequence.9</th>\n",
|
| 568 |
-
" <th>BindingDB Target Chain Sequence.10</th>\n",
|
| 569 |
-
" <th>BindingDB Target Chain Sequence.11</th>\n",
|
| 570 |
-
" <th>BindingDB Target Chain Sequence.12</th>\n",
|
| 571 |
-
" <th>affinity_uM</th>\n",
|
| 572 |
-
" </tr>\n",
|
| 573 |
-
" </thead>\n",
|
| 574 |
-
" <tbody>\n",
|
| 575 |
-
" <tr>\n",
|
| 576 |
-
" <th>0</th>\n",
|
| 577 |
-
" <td>COc1cc2c(Nc3ccc(Br)cc3F)ncnc2cc1OCC1CCN(C)CC1</td>\n",
|
| 578 |
-
" <td>0.24</td>\n",
|
| 579 |
-
" <td>None</td>\n",
|
| 580 |
-
" <td>None</td>\n",
|
| 581 |
-
" <td>None</td>\n",
|
| 582 |
-
" <td>None</td>\n",
|
| 583 |
-
" <td>PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKM...</td>\n",
|
| 584 |
-
" <td>None</td>\n",
|
| 585 |
-
" <td>None</td>\n",
|
| 586 |
-
" <td>None</td>\n",
|
| 587 |
-
" <td>None</td>\n",
|
| 588 |
-
" <td>None</td>\n",
|
| 589 |
-
" <td>None</td>\n",
|
| 590 |
-
" <td>None</td>\n",
|
| 591 |
-
" <td>None</td>\n",
|
| 592 |
-
" <td>None</td>\n",
|
| 593 |
-
" <td>None</td>\n",
|
| 594 |
-
" <td>None</td>\n",
|
| 595 |
-
" <td>None</td>\n",
|
| 596 |
-
" <td>0.00024</td>\n",
|
| 597 |
-
" </tr>\n",
|
| 598 |
-
" <tr>\n",
|
| 599 |
-
" <th>1</th>\n",
|
| 600 |
-
" <td>O[C@@H]1[C@@H](O)[C@@H](Cc2ccccc2)N(C\\C=C\\c2cn...</td>\n",
|
| 601 |
-
" <td>0.25</td>\n",
|
| 602 |
-
" <td>None</td>\n",
|
| 603 |
-
" <td>None</td>\n",
|
| 604 |
-
" <td>None</td>\n",
|
| 605 |
-
" <td>None</td>\n",
|
| 606 |
-
" <td>PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKM...</td>\n",
|
| 607 |
-
" <td>None</td>\n",
|
| 608 |
-
" <td>None</td>\n",
|
| 609 |
-
" <td>None</td>\n",
|
| 610 |
-
" <td>None</td>\n",
|
| 611 |
-
" <td>None</td>\n",
|
| 612 |
-
" <td>None</td>\n",
|
| 613 |
-
" <td>None</td>\n",
|
| 614 |
-
" <td>None</td>\n",
|
| 615 |
-
" <td>None</td>\n",
|
| 616 |
-
" <td>None</td>\n",
|
| 617 |
-
" <td>None</td>\n",
|
| 618 |
-
" <td>None</td>\n",
|
| 619 |
-
" <td>0.00025</td>\n",
|
| 620 |
-
" </tr>\n",
|
| 621 |
-
" <tr>\n",
|
| 622 |
-
" <th>2</th>\n",
|
| 623 |
-
" <td>O[C@@H]1[C@@H](O)[C@@H](Cc2ccccc2)N(CC2CC2)C(=...</td>\n",
|
| 624 |
-
" <td>0.41</td>\n",
|
| 625 |
-
" <td>None</td>\n",
|
| 626 |
-
" <td>None</td>\n",
|
| 627 |
-
" <td>None</td>\n",
|
| 628 |
-
" <td>None</td>\n",
|
| 629 |
-
" <td>PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKM...</td>\n",
|
| 630 |
-
" <td>None</td>\n",
|
| 631 |
-
" <td>None</td>\n",
|
| 632 |
-
" <td>None</td>\n",
|
| 633 |
-
" <td>None</td>\n",
|
| 634 |
-
" <td>None</td>\n",
|
| 635 |
-
" <td>None</td>\n",
|
| 636 |
-
" <td>None</td>\n",
|
| 637 |
-
" <td>None</td>\n",
|
| 638 |
-
" <td>None</td>\n",
|
| 639 |
-
" <td>None</td>\n",
|
| 640 |
-
" <td>None</td>\n",
|
| 641 |
-
" <td>None</td>\n",
|
| 642 |
-
" <td>0.00041</td>\n",
|
| 643 |
-
" </tr>\n",
|
| 644 |
-
" <tr>\n",
|
| 645 |
-
" <th>3</th>\n",
|
| 646 |
-
" <td>OCCCCCCN1[C@H](Cc2ccccc2)[C@H](O)[C@@H](O)[C@@...</td>\n",
|
| 647 |
-
" <td>0.8</td>\n",
|
| 648 |
-
" <td>None</td>\n",
|
| 649 |
-
" <td>None</td>\n",
|
| 650 |
-
" <td>None</td>\n",
|
| 651 |
-
" <td>None</td>\n",
|
| 652 |
-
" <td>PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKM...</td>\n",
|
| 653 |
-
" <td>None</td>\n",
|
| 654 |
-
" <td>None</td>\n",
|
| 655 |
-
" <td>None</td>\n",
|
| 656 |
-
" <td>None</td>\n",
|
| 657 |
-
" <td>None</td>\n",
|
| 658 |
-
" <td>None</td>\n",
|
| 659 |
-
" <td>None</td>\n",
|
| 660 |
-
" <td>None</td>\n",
|
| 661 |
-
" <td>None</td>\n",
|
| 662 |
-
" <td>None</td>\n",
|
| 663 |
-
" <td>None</td>\n",
|
| 664 |
-
" <td>None</td>\n",
|
| 665 |
-
" <td>0.00080</td>\n",
|
| 666 |
-
" </tr>\n",
|
| 667 |
-
" <tr>\n",
|
| 668 |
-
" <th>4</th>\n",
|
| 669 |
-
" <td>OCCCCCN1[C@H](Cc2ccccc2)[C@H](O)[C@@H](O)[C@@H...</td>\n",
|
| 670 |
-
" <td>0.99</td>\n",
|
| 671 |
-
" <td>None</td>\n",
|
| 672 |
-
" <td>None</td>\n",
|
| 673 |
-
" <td>None</td>\n",
|
| 674 |
-
" <td>None</td>\n",
|
| 675 |
-
" <td>PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKM...</td>\n",
|
| 676 |
-
" <td>None</td>\n",
|
| 677 |
-
" <td>None</td>\n",
|
| 678 |
-
" <td>None</td>\n",
|
| 679 |
-
" <td>None</td>\n",
|
| 680 |
-
" <td>None</td>\n",
|
| 681 |
-
" <td>None</td>\n",
|
| 682 |
-
" <td>None</td>\n",
|
| 683 |
-
" <td>None</td>\n",
|
| 684 |
-
" <td>None</td>\n",
|
| 685 |
-
" <td>None</td>\n",
|
| 686 |
-
" <td>None</td>\n",
|
| 687 |
-
" <td>None</td>\n",
|
| 688 |
-
" <td>0.00099</td>\n",
|
| 689 |
-
" </tr>\n",
|
| 690 |
-
" </tbody>\n",
|
| 691 |
-
"</table>\n",
|
| 692 |
-
"</div>"
|
| 693 |
-
],
|
| 694 |
-
"text/plain": [
|
| 695 |
-
" Ligand SMILES Ki (nM) IC50 (nM) \\\n",
|
| 696 |
-
"0 COc1cc2c(Nc3ccc(Br)cc3F)ncnc2cc1OCC1CCN(C)CC1 0.24 None \n",
|
| 697 |
-
"1 O[C@@H]1[C@@H](O)[C@@H](Cc2ccccc2)N(C\\C=C\\c2cn... 0.25 None \n",
|
| 698 |
-
"2 O[C@@H]1[C@@H](O)[C@@H](Cc2ccccc2)N(CC2CC2)C(=... 0.41 None \n",
|
| 699 |
-
"3 OCCCCCCN1[C@H](Cc2ccccc2)[C@H](O)[C@@H](O)[C@@... 0.8 None \n",
|
| 700 |
-
"4 OCCCCCN1[C@H](Cc2ccccc2)[C@H](O)[C@@H](O)[C@@H... 0.99 None \n",
|
| 701 |
-
"\n",
|
| 702 |
-
" Kd (nM) EC50 (nM) KEGG ID of Ligand \\\n",
|
| 703 |
-
"0 None None None \n",
|
| 704 |
-
"1 None None None \n",
|
| 705 |
-
"2 None None None \n",
|
| 706 |
-
"3 None None None \n",
|
| 707 |
-
"4 None None None \n",
|
| 708 |
-
"\n",
|
| 709 |
-
" seq \\\n",
|
| 710 |
-
"0 PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKM... \n",
|
| 711 |
-
"1 PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKM... \n",
|
| 712 |
-
"2 PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKM... \n",
|
| 713 |
-
"3 PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKM... \n",
|
| 714 |
-
"4 PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKM... \n",
|
| 715 |
-
"\n",
|
| 716 |
-
" BindingDB Target Chain Sequence.1 BindingDB Target Chain Sequence.2 \\\n",
|
| 717 |
-
"0 None None \n",
|
| 718 |
-
"1 None None \n",
|
| 719 |
-
"2 None None \n",
|
| 720 |
-
"3 None None \n",
|
| 721 |
-
"4 None None \n",
|
| 722 |
-
"\n",
|
| 723 |
-
" BindingDB Target Chain Sequence.3 BindingDB Target Chain Sequence.4 \\\n",
|
| 724 |
-
"0 None None \n",
|
| 725 |
-
"1 None None \n",
|
| 726 |
-
"2 None None \n",
|
| 727 |
-
"3 None None \n",
|
| 728 |
-
"4 None None \n",
|
| 729 |
-
"\n",
|
| 730 |
-
" BindingDB Target Chain Sequence.5 BindingDB Target Chain Sequence.6 \\\n",
|
| 731 |
-
"0 None None \n",
|
| 732 |
-
"1 None None \n",
|
| 733 |
-
"2 None None \n",
|
| 734 |
-
"3 None None \n",
|
| 735 |
-
"4 None None \n",
|
| 736 |
-
"\n",
|
| 737 |
-
" BindingDB Target Chain Sequence.7 BindingDB Target Chain Sequence.8 \\\n",
|
| 738 |
-
"0 None None \n",
|
| 739 |
-
"1 None None \n",
|
| 740 |
-
"2 None None \n",
|
| 741 |
-
"3 None None \n",
|
| 742 |
-
"4 None None \n",
|
| 743 |
-
"\n",
|
| 744 |
-
" BindingDB Target Chain Sequence.9 BindingDB Target Chain Sequence.10 \\\n",
|
| 745 |
-
"0 None None \n",
|
| 746 |
-
"1 None None \n",
|
| 747 |
-
"2 None None \n",
|
| 748 |
-
"3 None None \n",
|
| 749 |
-
"4 None None \n",
|
| 750 |
-
"\n",
|
| 751 |
-
" BindingDB Target Chain Sequence.11 BindingDB Target Chain Sequence.12 \\\n",
|
| 752 |
-
"0 None None \n",
|
| 753 |
-
"1 None None \n",
|
| 754 |
-
"2 None None \n",
|
| 755 |
-
"3 None None \n",
|
| 756 |
-
"4 None None \n",
|
| 757 |
-
"\n",
|
| 758 |
-
" affinity_uM \n",
|
| 759 |
-
"0 0.00024 \n",
|
| 760 |
-
"1 0.00025 \n",
|
| 761 |
-
"2 0.00041 \n",
|
| 762 |
-
"3 0.00080 \n",
|
| 763 |
-
"4 0.00099 "
|
| 764 |
-
]
|
| 765 |
-
},
|
| 766 |
-
"execution_count": 29,
|
| 767 |
-
"metadata": {},
|
| 768 |
-
"output_type": "execute_result"
|
| 769 |
-
}
|
| 770 |
-
],
|
| 771 |
-
"source": [
|
| 772 |
-
"df_affinity.head()"
|
| 773 |
-
]
|
| 774 |
-
},
|
| 775 |
-
{
|
| 776 |
-
"cell_type": "code",
|
| 777 |
-
"execution_count": 30,
|
| 778 |
-
"id": "603fd298-0aa6-4097-b298-c55db013548c",
|
| 779 |
-
"metadata": {},
|
| 780 |
-
"outputs": [
|
| 781 |
-
{
|
| 782 |
-
"data": {
|
| 783 |
-
"text/plain": [
|
| 784 |
-
"2219812"
|
| 785 |
-
]
|
| 786 |
-
},
|
| 787 |
-
"execution_count": 30,
|
| 788 |
-
"metadata": {},
|
| 789 |
-
"output_type": "execute_result"
|
| 790 |
-
}
|
| 791 |
-
],
|
| 792 |
-
"source": [
|
| 793 |
-
"len(df_affinity)"
|
| 794 |
-
]
|
| 795 |
-
},
|
| 796 |
-
{
|
| 797 |
-
"cell_type": "code",
|
| 798 |
-
"execution_count": 31,
|
| 799 |
-
"id": "d95ad9a9-d4ca-4679-8a33-235fe6e7047f",
|
| 800 |
-
"metadata": {},
|
| 801 |
-
"outputs": [
|
| 802 |
-
{
|
| 803 |
-
"data": {
|
| 804 |
-
"text/plain": [
|
| 805 |
-
"2219812"
|
| 806 |
-
]
|
| 807 |
-
},
|
| 808 |
-
"execution_count": 31,
|
| 809 |
-
"metadata": {},
|
| 810 |
-
"output_type": "execute_result"
|
| 811 |
-
}
|
| 812 |
-
],
|
| 813 |
-
"source": [
|
| 814 |
-
"len(df_affinity[~df_affinity['affinity_uM'].isnull()])"
|
| 815 |
-
]
|
| 816 |
-
},
|
| 817 |
-
{
|
| 818 |
-
"cell_type": "code",
|
| 819 |
-
"execution_count": 25,
|
| 820 |
-
"id": "20690729",
|
| 821 |
-
"metadata": {},
|
| 822 |
-
"outputs": [],
|
| 823 |
-
"source": [
|
| 824 |
-
"import rdkit.Chem as Chem"
|
| 825 |
-
]
|
| 826 |
-
},
|
| 827 |
-
{
|
| 828 |
-
"cell_type": "code",
|
| 829 |
-
"execution_count": 27,
|
| 830 |
-
"id": "48114dcc",
|
| 831 |
-
"metadata": {},
|
| 832 |
-
"outputs": [],
|
| 833 |
-
"source": [
|
| 834 |
-
"df_pdb = df[~df['PDB ID(s) for Ligand-Target Complex'].isnull()][['PDB ID(s) for Ligand-Target Complex','Ligand SMILES']]"
|
| 835 |
-
]
|
| 836 |
-
},
|
| 837 |
-
{
|
| 838 |
-
"cell_type": "code",
|
| 839 |
-
"execution_count": 28,
|
| 840 |
-
"id": "caa0497c",
|
| 841 |
-
"metadata": {},
|
| 842 |
-
"outputs": [],
|
| 843 |
-
"source": [
|
| 844 |
-
"def make_canonical(smi):\n",
|
| 845 |
-
" return Chem.MolToSmiles(Chem.MolFromSmiles(smi))\n",
|
| 846 |
-
"\n",
|
| 847 |
-
"df_pdb['can_smiles'] = df_pdb['Ligand SMILES'].apply(make_canonical)"
|
| 848 |
-
]
|
| 849 |
-
},
|
| 850 |
-
{
|
| 851 |
-
"cell_type": "code",
|
| 852 |
-
"execution_count": 29,
|
| 853 |
-
"id": "e82d64f3",
|
| 854 |
-
"metadata": {},
|
| 855 |
-
"outputs": [
|
| 856 |
-
{
|
| 857 |
-
"data": {
|
| 858 |
-
"text/html": [
|
| 859 |
-
"<div>\n",
|
| 860 |
-
"<style scoped>\n",
|
| 861 |
-
" .dataframe tbody tr th:only-of-type {\n",
|
| 862 |
-
" vertical-align: middle;\n",
|
| 863 |
-
" }\n",
|
| 864 |
-
"\n",
|
| 865 |
-
" .dataframe tbody tr th {\n",
|
| 866 |
-
" vertical-align: top;\n",
|
| 867 |
-
" }\n",
|
| 868 |
-
"\n",
|
| 869 |
-
" .dataframe thead th {\n",
|
| 870 |
-
" text-align: right;\n",
|
| 871 |
-
" }\n",
|
| 872 |
-
"</style>\n",
|
| 873 |
-
"<table border=\"1\" class=\"dataframe\">\n",
|
| 874 |
-
" <thead>\n",
|
| 875 |
-
" <tr style=\"text-align: right;\">\n",
|
| 876 |
-
" <th></th>\n",
|
| 877 |
-
" <th>PDB ID(s) for Ligand-Target Complex</th>\n",
|
| 878 |
-
" <th>Ligand SMILES</th>\n",
|
| 879 |
-
" <th>can_smiles</th>\n",
|
| 880 |
-
" </tr>\n",
|
| 881 |
-
" </thead>\n",
|
| 882 |
-
" <tbody>\n",
|
| 883 |
-
" <tr>\n",
|
| 884 |
-
" <th>0</th>\n",
|
| 885 |
-
" <td>2IVU</td>\n",
|
| 886 |
-
" <td>COc1cc2c(Nc3ccc(Br)cc3F)ncnc2cc1OCC1CCN(C)CC1</td>\n",
|
| 887 |
-
" <td>COc1cc2c(Nc3ccc(Br)cc3F)ncnc2cc1OCC1CCN(C)CC1</td>\n",
|
| 888 |
-
" </tr>\n",
|
| 889 |
-
" <tr>\n",
|
| 890 |
-
" <th>29</th>\n",
|
| 891 |
-
" <td>1HWR</td>\n",
|
| 892 |
-
" <td>O[C@@H]1[C@@H](O)[C@@H](Cc2ccccc2)N(CC=C)C(=O)...</td>\n",
|
| 893 |
-
" <td>C=CCN1C(=O)N(CC=C)[C@H](Cc2ccccc2)[C@H](O)[C@@...</td>\n",
|
| 894 |
-
" </tr>\n",
|
| 895 |
-
" <tr>\n",
|
| 896 |
-
" <th>34</th>\n",
|
| 897 |
-
" <td>6DGY,6DH1,6DH4,6DH7,3O99</td>\n",
|
| 898 |
-
" <td>CC[C@H](C)CN(C[C@@H](O)[C@H](Cc1ccccc1)NC(=O)O...</td>\n",
|
| 899 |
-
" <td>CC[C@H](C)CN(C[C@@H](O)[C@H](Cc1ccccc1)NC(=O)O...</td>\n",
|
| 900 |
-
" </tr>\n",
|
| 901 |
-
" <tr>\n",
|
| 902 |
-
" <th>129</th>\n",
|
| 903 |
-
" <td>1MES,1MEU,1MET,1BVG,1BVE,3JVW,1QBS</td>\n",
|
| 904 |
-
" <td>OCc1ccc(CN2[C@H](Cc3ccccc3)[C@H](O)[C@@H](O)[C...</td>\n",
|
| 905 |
-
" <td>O=C1N(Cc2ccc(CO)cc2)[C@H](Cc2ccccc2)[C@H](O)[C...</td>\n",
|
| 906 |
-
" </tr>\n",
|
| 907 |
-
" <tr>\n",
|
| 908 |
-
" <th>130</th>\n",
|
| 909 |
-
" <td>1MER,1DMP,1RQ9</td>\n",
|
| 910 |
-
" <td>Nc1cccc(CN2[C@H](Cc3ccccc3)[C@H](O)[C@@H](O)[C...</td>\n",
|
| 911 |
-
" <td>Nc1cccc(CN2C(=O)N(Cc3cccc(N)c3)[C@H](Cc3ccccc3...</td>\n",
|
| 912 |
-
" </tr>\n",
|
| 913 |
-
" <tr>\n",
|
| 914 |
-
" <th>...</th>\n",
|
| 915 |
-
" <td>...</td>\n",
|
| 916 |
-
" <td>...</td>\n",
|
| 917 |
-
" <td>...</td>\n",
|
| 918 |
-
" </tr>\n",
|
| 919 |
-
" <tr>\n",
|
| 920 |
-
" <th>2333375</th>\n",
|
| 921 |
-
" <td>1MUI,2RKG,2RKF,1RV7,2Q5K,2O4S,6DJ1,6DJ2,3OGQ,2...</td>\n",
|
| 922 |
-
" <td>CC(C)[C@H](N1CCCNC1=O)C(=O)N[C@H](C[C@H](O)[C@...</td>\n",
|
| 923 |
-
" <td>Cc1cccc(C)c1OCC(=O)N[C@@H](Cc1ccccc1)[C@@H](O)...</td>\n",
|
| 924 |
-
" </tr>\n",
|
| 925 |
-
" <tr>\n",
|
| 926 |
-
" <th>2333376</th>\n",
|
| 927 |
-
" <td>4NPT,4DQH,4DQE,5E5J,3JW2,6OPU,6OPX,2HS2,2HS1,2...</td>\n",
|
| 928 |
-
" <td>CC(C)CN(C[C@@H](O)[C@H](Cc1ccccc1)NC(=O)O[C@H]...</td>\n",
|
| 929 |
-
" <td>CC(C)CN(C[C@@H](O)[C@H](Cc1ccccc1)NC(=O)O[C@H]...</td>\n",
|
| 930 |
-
" </tr>\n",
|
| 931 |
-
" <tr>\n",
|
| 932 |
-
" <th>2333380</th>\n",
|
| 933 |
-
" <td>6EKZ,1DY4,5FUK,6PS5</td>\n",
|
| 934 |
-
" <td>CC(C)NCC(O)COc1cccc2ccccc12</td>\n",
|
| 935 |
-
" <td>CC(C)NCC(O)COc1cccc2ccccc12</td>\n",
|
| 936 |
-
" </tr>\n",
|
| 937 |
-
" <tr>\n",
|
| 938 |
-
" <th>2333384</th>\n",
|
| 939 |
-
" <td>6EKZ,1DY4,5FUK,6PS5</td>\n",
|
| 940 |
-
" <td>CC(C)NCC(O)COc1cccc2ccccc12</td>\n",
|
| 941 |
-
" <td>CC(C)NCC(O)COc1cccc2ccccc12</td>\n",
|
| 942 |
-
" </tr>\n",
|
| 943 |
-
" <tr>\n",
|
| 944 |
-
" <th>2333385</th>\n",
|
| 945 |
-
" <td>6A60,3DCT</td>\n",
|
| 946 |
-
" <td>CC(C)c1onc(c1COc1ccc(\\C=C\\c2cccc(c2)C(O)=O)c(C...</td>\n",
|
| 947 |
-
" <td>CC(C)c1onc(-c2c(Cl)cccc2Cl)c1COc1ccc(/C=C/c2cc...</td>\n",
|
| 948 |
-
" </tr>\n",
|
| 949 |
-
" </tbody>\n",
|
| 950 |
-
"</table>\n",
|
| 951 |
-
"<p>123385 rows × 3 columns</p>\n",
|
| 952 |
-
"</div>"
|
| 953 |
-
],
|
| 954 |
-
"text/plain": [
|
| 955 |
-
" PDB ID(s) for Ligand-Target Complex \\\n",
|
| 956 |
-
"0 2IVU \n",
|
| 957 |
-
"29 1HWR \n",
|
| 958 |
-
"34 6DGY,6DH1,6DH4,6DH7,3O99 \n",
|
| 959 |
-
"129 1MES,1MEU,1MET,1BVG,1BVE,3JVW,1QBS \n",
|
| 960 |
-
"130 1MER,1DMP,1RQ9 \n",
|
| 961 |
-
"... ... \n",
|
| 962 |
-
"2333375 1MUI,2RKG,2RKF,1RV7,2Q5K,2O4S,6DJ1,6DJ2,3OGQ,2... \n",
|
| 963 |
-
"2333376 4NPT,4DQH,4DQE,5E5J,3JW2,6OPU,6OPX,2HS2,2HS1,2... \n",
|
| 964 |
-
"2333380 6EKZ,1DY4,5FUK,6PS5 \n",
|
| 965 |
-
"2333384 6EKZ,1DY4,5FUK,6PS5 \n",
|
| 966 |
-
"2333385 6A60,3DCT \n",
|
| 967 |
-
"\n",
|
| 968 |
-
" Ligand SMILES \\\n",
|
| 969 |
-
"0 COc1cc2c(Nc3ccc(Br)cc3F)ncnc2cc1OCC1CCN(C)CC1 \n",
|
| 970 |
-
"29 O[C@@H]1[C@@H](O)[C@@H](Cc2ccccc2)N(CC=C)C(=O)... \n",
|
| 971 |
-
"34 CC[C@H](C)CN(C[C@@H](O)[C@H](Cc1ccccc1)NC(=O)O... \n",
|
| 972 |
-
"129 OCc1ccc(CN2[C@H](Cc3ccccc3)[C@H](O)[C@@H](O)[C... \n",
|
| 973 |
-
"130 Nc1cccc(CN2[C@H](Cc3ccccc3)[C@H](O)[C@@H](O)[C... \n",
|
| 974 |
-
"... ... \n",
|
| 975 |
-
"2333375 CC(C)[C@H](N1CCCNC1=O)C(=O)N[C@H](C[C@H](O)[C@... \n",
|
| 976 |
-
"2333376 CC(C)CN(C[C@@H](O)[C@H](Cc1ccccc1)NC(=O)O[C@H]... \n",
|
| 977 |
-
"2333380 CC(C)NCC(O)COc1cccc2ccccc12 \n",
|
| 978 |
-
"2333384 CC(C)NCC(O)COc1cccc2ccccc12 \n",
|
| 979 |
-
"2333385 CC(C)c1onc(c1COc1ccc(\\C=C\\c2cccc(c2)C(O)=O)c(C... \n",
|
| 980 |
-
"\n",
|
| 981 |
-
" can_smiles \n",
|
| 982 |
-
"0 COc1cc2c(Nc3ccc(Br)cc3F)ncnc2cc1OCC1CCN(C)CC1 \n",
|
| 983 |
-
"29 C=CCN1C(=O)N(CC=C)[C@H](Cc2ccccc2)[C@H](O)[C@@... \n",
|
| 984 |
-
"34 CC[C@H](C)CN(C[C@@H](O)[C@H](Cc1ccccc1)NC(=O)O... \n",
|
| 985 |
-
"129 O=C1N(Cc2ccc(CO)cc2)[C@H](Cc2ccccc2)[C@H](O)[C... \n",
|
| 986 |
-
"130 Nc1cccc(CN2C(=O)N(Cc3cccc(N)c3)[C@H](Cc3ccccc3... \n",
|
| 987 |
-
"... ... \n",
|
| 988 |
-
"2333375 Cc1cccc(C)c1OCC(=O)N[C@@H](Cc1ccccc1)[C@@H](O)... \n",
|
| 989 |
-
"2333376 CC(C)CN(C[C@@H](O)[C@H](Cc1ccccc1)NC(=O)O[C@H]... \n",
|
| 990 |
-
"2333380 CC(C)NCC(O)COc1cccc2ccccc12 \n",
|
| 991 |
-
"2333384 CC(C)NCC(O)COc1cccc2ccccc12 \n",
|
| 992 |
-
"2333385 CC(C)c1onc(-c2c(Cl)cccc2Cl)c1COc1ccc(/C=C/c2cc... \n",
|
| 993 |
-
"\n",
|
| 994 |
-
"[123385 rows x 3 columns]"
|
| 995 |
-
]
|
| 996 |
-
},
|
| 997 |
-
"execution_count": 29,
|
| 998 |
-
"metadata": {},
|
| 999 |
-
"output_type": "execute_result"
|
| 1000 |
-
}
|
| 1001 |
-
],
|
| 1002 |
-
"source": [
|
| 1003 |
-
"df_pdb"
|
| 1004 |
-
]
|
| 1005 |
-
},
|
| 1006 |
-
{
|
| 1007 |
-
"cell_type": "code",
|
| 1008 |
-
"execution_count": null,
|
| 1009 |
-
"id": "593c9aec",
|
| 1010 |
-
"metadata": {},
|
| 1011 |
-
"outputs": [],
|
| 1012 |
-
"source": []
|
| 1013 |
-
}
|
| 1014 |
-
],
|
| 1015 |
-
"metadata": {
|
| 1016 |
-
"kernelspec": {
|
| 1017 |
-
"display_name": "Python 3",
|
| 1018 |
-
"language": "python",
|
| 1019 |
-
"name": "python3"
|
| 1020 |
-
},
|
| 1021 |
-
"language_info": {
|
| 1022 |
-
"codemirror_mode": {
|
| 1023 |
-
"name": "ipython",
|
| 1024 |
-
"version": 3
|
| 1025 |
-
},
|
| 1026 |
-
"file_extension": ".py",
|
| 1027 |
-
"mimetype": "text/x-python",
|
| 1028 |
-
"name": "python",
|
| 1029 |
-
"nbconvert_exporter": "python",
|
| 1030 |
-
"pygments_lexer": "ipython3",
|
| 1031 |
-
"version": "3.9.4"
|
| 1032 |
-
}
|
| 1033 |
-
},
|
| 1034 |
-
"nbformat": 4,
|
| 1035 |
-
"nbformat_minor": 5
|
| 1036 |
-
}
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|
biolip.ipynb
DELETED
|
@@ -1,586 +0,0 @@
|
|
| 1 |
-
{
|
| 2 |
-
"cells": [
|
| 3 |
-
{
|
| 4 |
-
"cell_type": "code",
|
| 5 |
-
"execution_count": 3,
|
| 6 |
-
"id": "26bc18a2-a6eb-49d3-be80-876ddc7dd8e1",
|
| 7 |
-
"metadata": {},
|
| 8 |
-
"outputs": [],
|
| 9 |
-
"source": [
|
| 10 |
-
"import pandas as pd"
|
| 11 |
-
]
|
| 12 |
-
},
|
| 13 |
-
{
|
| 14 |
-
"cell_type": "markdown",
|
| 15 |
-
"id": "dbef8a5d-8603-4f40-aa98-fe50aa1160f6",
|
| 16 |
-
"metadata": {},
|
| 17 |
-
"source": [
|
| 18 |
-
"the binding affinities are **only** present in the 2013 snapshot, not in the updated ones"
|
| 19 |
-
]
|
| 20 |
-
},
|
| 21 |
-
{
|
| 22 |
-
"cell_type": "code",
|
| 23 |
-
"execution_count": 48,
|
| 24 |
-
"id": "3b59cfb4-c42a-425d-9653-44f07f9e864e",
|
| 25 |
-
"metadata": {},
|
| 26 |
-
"outputs": [],
|
| 27 |
-
"source": [
|
| 28 |
-
"df = pd.read_table('biolip/data/BioLiP_2013-03-6_nr.txt',sep='\\t',header=None,usecols=[0,4,5,6,13,14,15,16,19])\n",
|
| 29 |
-
"df = df.rename(columns={0:'pdb',4:'chain',5:'l_id',6:'l_chain',\n",
|
| 30 |
-
" 13: 'affinity_lit',14: 'affinity_moad',15: 'affinity_pdbbind-cn',16:'affinity_bindingdb',\n",
|
| 31 |
-
" 19: 'seq'})"
|
| 32 |
-
]
|
| 33 |
-
},
|
| 34 |
-
{
|
| 35 |
-
"cell_type": "code",
|
| 36 |
-
"execution_count": 49,
|
| 37 |
-
"id": "01123edd-2b98-4fcc-a2e9-28213b9bed82",
|
| 38 |
-
"metadata": {},
|
| 39 |
-
"outputs": [],
|
| 40 |
-
"source": [
|
| 41 |
-
"base = 'biolip/data/ligand/'\n",
|
| 42 |
-
"df['ligand_fn'] = base + df['pdb']+'_'+df['chain']+'_'+df['l_id'].astype(str)+'_'+df['l_chain'].astype(str)+'.pdb'"
|
| 43 |
-
]
|
| 44 |
-
},
|
| 45 |
-
{
|
| 46 |
-
"cell_type": "code",
|
| 47 |
-
"execution_count": 50,
|
| 48 |
-
"id": "bd8671da-66ad-40ad-b221-e33228be65f4",
|
| 49 |
-
"metadata": {},
|
| 50 |
-
"outputs": [],
|
| 51 |
-
"source": [
|
| 52 |
-
"df_complex = pd.read_parquet('data/biolip_complex.parquet')"
|
| 53 |
-
]
|
| 54 |
-
},
|
| 55 |
-
{
|
| 56 |
-
"cell_type": "code",
|
| 57 |
-
"execution_count": 51,
|
| 58 |
-
"id": "08b04d75-c01e-4b26-ae2d-622efae3bd1f",
|
| 59 |
-
"metadata": {},
|
| 60 |
-
"outputs": [],
|
| 61 |
-
"source": [
|
| 62 |
-
"df_affinity = df_complex[~df_complex['affinity_lit'].isnull() | ~df_complex['affinity_moad'].isnull() \n",
|
| 63 |
-
" | ~df_complex['affinity_pdbbind-cn'].isnull() | ~df_complex['affinity_bindingdb'].isnull()].copy()"
|
| 64 |
-
]
|
| 65 |
-
},
|
| 66 |
-
{
|
| 67 |
-
"cell_type": "code",
|
| 68 |
-
"execution_count": 57,
|
| 69 |
-
"id": "97af5533-10fe-4419-a998-ed80b7d26690",
|
| 70 |
-
"metadata": {},
|
| 71 |
-
"outputs": [],
|
| 72 |
-
"source": [
|
| 73 |
-
"from pint import UnitRegistry\n",
|
| 74 |
-
"import numpy as np\n",
|
| 75 |
-
"import re\n",
|
| 76 |
-
"ureg = UnitRegistry()\n",
|
| 77 |
-
"\n",
|
| 78 |
-
"quantities = ['ki','kd','ka','k1/2','kb','ic50','ec50','km']\n",
|
| 79 |
-
"\n",
|
| 80 |
-
"others = set()\n",
|
| 81 |
-
"def to_uM(affinities):\n",
|
| 82 |
-
" lit, moad, pdbbind, bindingdb = affinities\n",
|
| 83 |
-
"\n",
|
| 84 |
-
" vals = []\n",
|
| 85 |
-
" try:\n",
|
| 86 |
-
" q = re.split('[=~<>]',str(lit))[0].lower()\n",
|
| 87 |
-
" if q not in quantities:\n",
|
| 88 |
-
" others.add(q)\n",
|
| 89 |
-
" raise\n",
|
| 90 |
-
" val = re.split('[=~<>]',str(lit))[1].split(' ')[0]\n",
|
| 91 |
-
" val = ureg(val).m_as(ureg.uM)\n",
|
| 92 |
-
" vals.append(val)\n",
|
| 93 |
-
" except:\n",
|
| 94 |
-
" pass\n",
|
| 95 |
-
"\n",
|
| 96 |
-
" try:\n",
|
| 97 |
-
" q = re.split('[=~<>]',str(lit))[0].lower()\n",
|
| 98 |
-
" if q not in quantities:\n",
|
| 99 |
-
" others.add(q)\n",
|
| 100 |
-
" raise\n",
|
| 101 |
-
" val = re.split('[=~<>]',str(lit))[1].split(' ')[0]\n",
|
| 102 |
-
" val = ureg(val).m_as(1/ureg.uM)\n",
|
| 103 |
-
" vals.append(1/val)\n",
|
| 104 |
-
" except:\n",
|
| 105 |
-
" pass\n",
|
| 106 |
-
"\n",
|
| 107 |
-
" try:\n",
|
| 108 |
-
" q = re.split('[=~<>]',str(moad))[0].lower()\n",
|
| 109 |
-
" if q not in quantities:\n",
|
| 110 |
-
" others.add(q)\n",
|
| 111 |
-
" raise\n",
|
| 112 |
-
" val = re.split('[=~<>]',str(moad))[1].split(' ')[0]\n",
|
| 113 |
-
" val = ureg(val).m_as(ureg.uM)\n",
|
| 114 |
-
" vals.append(val)\n",
|
| 115 |
-
" except:\n",
|
| 116 |
-
" pass\n",
|
| 117 |
-
"\n",
|
| 118 |
-
" try:\n",
|
| 119 |
-
" q = re.split('[=~<>]',str(moad))[0].lower()\n",
|
| 120 |
-
" if q not in quantities:\n",
|
| 121 |
-
" others.add(q)\n",
|
| 122 |
-
" raise\n",
|
| 123 |
-
" val = re.split('[=~<>]',str(moad))[1].split(' ')[0]\n",
|
| 124 |
-
" val = ureg(val).m_as(1/ureg.uM)\n",
|
| 125 |
-
" vals.append(1/moad)\n",
|
| 126 |
-
" except:\n",
|
| 127 |
-
" pass\n",
|
| 128 |
-
"\n",
|
| 129 |
-
" try:\n",
|
| 130 |
-
" q = re.split('[=~<>]',str(pdbbind))[0].lower()\n",
|
| 131 |
-
" if q not in quantities:\n",
|
| 132 |
-
" others.add(q)\n",
|
| 133 |
-
" raise\n",
|
| 134 |
-
" val = re.split('[=~<>]',str(pdbbind))[1].split(' ')[0]\n",
|
| 135 |
-
" val = ureg(val).m_as(ureg.uM)\n",
|
| 136 |
-
" vals.append(val)\n",
|
| 137 |
-
" except:\n",
|
| 138 |
-
" pass\n",
|
| 139 |
-
"\n",
|
| 140 |
-
" try:\n",
|
| 141 |
-
" q = re.split('[=~<>]',str(pdbbind))[0].lower()\n",
|
| 142 |
-
" if q not in quantities:\n",
|
| 143 |
-
" others.add(q)\n",
|
| 144 |
-
" raise\n",
|
| 145 |
-
" val = re.split('[=~<>]',str(pdbbind))[1].split(' ')[0]\n",
|
| 146 |
-
" val = ureg(val).m_as(1/ureg.uM)\n",
|
| 147 |
-
" vals.append(1/val)\n",
|
| 148 |
-
" except:\n",
|
| 149 |
-
" pass\n",
|
| 150 |
-
"\n",
|
| 151 |
-
" try:\n",
|
| 152 |
-
" q = re.split('[=~<>]',str(bindingdb))[0].lower()\n",
|
| 153 |
-
" if q not in quantities:\n",
|
| 154 |
-
" others.add(q)\n",
|
| 155 |
-
" raise\n",
|
| 156 |
-
" val = re.split('[=~<>]',str(bindingdb))[1].split(' ')[0]\n",
|
| 157 |
-
" val = ureg(val).m_as(ureg.uM)\n",
|
| 158 |
-
" vals.append(val)\n",
|
| 159 |
-
" except:\n",
|
| 160 |
-
" pass\n",
|
| 161 |
-
"\n",
|
| 162 |
-
" try:\n",
|
| 163 |
-
" q = re.split('[=~<>]',str(bindingdb))[0].lower()\n",
|
| 164 |
-
" if q not in quantities:\n",
|
| 165 |
-
" others.add(q)\n",
|
| 166 |
-
" raise\n",
|
| 167 |
-
" val = re.split('[=~<>]',str(bindingdb))[1].split(' ')[0]\n",
|
| 168 |
-
" val = ureg(val).m_as(1/ureg.uM)\n",
|
| 169 |
-
" vals.append(1/val)\n",
|
| 170 |
-
" except:\n",
|
| 171 |
-
" pass\n",
|
| 172 |
-
"\n",
|
| 173 |
-
" if len(vals) > 0:\n",
|
| 174 |
-
" vals = np.array(vals)\n",
|
| 175 |
-
" return np.mean(vals[~np.isnan(vals)])\n",
|
| 176 |
-
" \n",
|
| 177 |
-
" return None"
|
| 178 |
-
]
|
| 179 |
-
},
|
| 180 |
-
{
|
| 181 |
-
"cell_type": "code",
|
| 182 |
-
"execution_count": 58,
|
| 183 |
-
"id": "d2fab1e6-ec5b-46f9-a4a2-f3744128c777",
|
| 184 |
-
"metadata": {},
|
| 185 |
-
"outputs": [
|
| 186 |
-
{
|
| 187 |
-
"data": {
|
| 188 |
-
"text/plain": [
|
| 189 |
-
"Index(['pdb', 'chain', 'l_id', 'l_chain', 'affinity_lit', 'affinity_moad',\n",
|
| 190 |
-
" 'affinity_pdbbind-cn', 'affinity_bindingdb', 'seq', 'ligand_fn',\n",
|
| 191 |
-
" 'smiles', 'affinity_uM'],\n",
|
| 192 |
-
" dtype='object')"
|
| 193 |
-
]
|
| 194 |
-
},
|
| 195 |
-
"execution_count": 58,
|
| 196 |
-
"metadata": {},
|
| 197 |
-
"output_type": "execute_result"
|
| 198 |
-
}
|
| 199 |
-
],
|
| 200 |
-
"source": [
|
| 201 |
-
"df_affinity.columns"
|
| 202 |
-
]
|
| 203 |
-
},
|
| 204 |
-
{
|
| 205 |
-
"cell_type": "code",
|
| 206 |
-
"execution_count": 59,
|
| 207 |
-
"id": "e21154a9-d3a0-4aa3-986f-cfeebc280da6",
|
| 208 |
-
"metadata": {},
|
| 209 |
-
"outputs": [],
|
| 210 |
-
"source": [
|
| 211 |
-
"df_affinity['affinity_uM'] = df_affinity[['affinity_lit','affinity_moad','affinity_pdbbind-cn','affinity_bindingdb']].apply(to_uM,axis=1)"
|
| 212 |
-
]
|
| 213 |
-
},
|
| 214 |
-
{
|
| 215 |
-
"cell_type": "code",
|
| 216 |
-
"execution_count": 60,
|
| 217 |
-
"id": "6b9526ec-b134-4ecb-8fea-b493c3fdac22",
|
| 218 |
-
"metadata": {},
|
| 219 |
-
"outputs": [
|
| 220 |
-
{
|
| 221 |
-
"data": {
|
| 222 |
-
"text/plain": [
|
| 223 |
-
"{'deltag', 'deltah', 'none'}"
|
| 224 |
-
]
|
| 225 |
-
},
|
| 226 |
-
"execution_count": 60,
|
| 227 |
-
"metadata": {},
|
| 228 |
-
"output_type": "execute_result"
|
| 229 |
-
}
|
| 230 |
-
],
|
| 231 |
-
"source": [
|
| 232 |
-
"others"
|
| 233 |
-
]
|
| 234 |
-
},
|
| 235 |
-
{
|
| 236 |
-
"cell_type": "code",
|
| 237 |
-
"execution_count": 61,
|
| 238 |
-
"id": "0fc94de0-823d-4f4f-9904-1c4d1e722c2e",
|
| 239 |
-
"metadata": {},
|
| 240 |
-
"outputs": [
|
| 241 |
-
{
|
| 242 |
-
"data": {
|
| 243 |
-
"text/html": [
|
| 244 |
-
"<div>\n",
|
| 245 |
-
"<style scoped>\n",
|
| 246 |
-
" .dataframe tbody tr th:only-of-type {\n",
|
| 247 |
-
" vertical-align: middle;\n",
|
| 248 |
-
" }\n",
|
| 249 |
-
"\n",
|
| 250 |
-
" .dataframe tbody tr th {\n",
|
| 251 |
-
" vertical-align: top;\n",
|
| 252 |
-
" }\n",
|
| 253 |
-
"\n",
|
| 254 |
-
" .dataframe thead th {\n",
|
| 255 |
-
" text-align: right;\n",
|
| 256 |
-
" }\n",
|
| 257 |
-
"</style>\n",
|
| 258 |
-
"<table border=\"1\" class=\"dataframe\">\n",
|
| 259 |
-
" <thead>\n",
|
| 260 |
-
" <tr style=\"text-align: right;\">\n",
|
| 261 |
-
" <th></th>\n",
|
| 262 |
-
" <th>pdb</th>\n",
|
| 263 |
-
" <th>chain</th>\n",
|
| 264 |
-
" <th>l_id</th>\n",
|
| 265 |
-
" <th>l_chain</th>\n",
|
| 266 |
-
" <th>affinity_lit</th>\n",
|
| 267 |
-
" <th>affinity_moad</th>\n",
|
| 268 |
-
" <th>affinity_pdbbind-cn</th>\n",
|
| 269 |
-
" <th>affinity_bindingdb</th>\n",
|
| 270 |
-
" <th>seq</th>\n",
|
| 271 |
-
" <th>ligand_fn</th>\n",
|
| 272 |
-
" <th>smiles</th>\n",
|
| 273 |
-
" <th>affinity_uM</th>\n",
|
| 274 |
-
" </tr>\n",
|
| 275 |
-
" </thead>\n",
|
| 276 |
-
" <tbody>\n",
|
| 277 |
-
" <tr>\n",
|
| 278 |
-
" <th>38</th>\n",
|
| 279 |
-
" <td>11gs</td>\n",
|
| 280 |
-
" <td>EAA</td>\n",
|
| 281 |
-
" <td>A</td>\n",
|
| 282 |
-
" <td>1</td>\n",
|
| 283 |
-
" <td>None</td>\n",
|
| 284 |
-
" <td>ki=1.5uM (GTT EAA)</td>\n",
|
| 285 |
-
" <td>Ki=1.5uM (GTT-EAA)</td>\n",
|
| 286 |
-
" <td>None</td>\n",
|
| 287 |
-
" <td>PYTVVYFPVRGRCAALRMLLADQGQSWKEEVVTVETWQEGSLKASC...</td>\n",
|
| 288 |
-
" <td>biolip/data/ligand/11gs_EAA_A_1.pdb</td>\n",
|
| 289 |
-
" <td>CC[C@H](C(=O)c1ccc(c(c1Cl)Cl)OCC(=O)O)C</td>\n",
|
| 290 |
-
" <td>1.5000</td>\n",
|
| 291 |
-
" </tr>\n",
|
| 292 |
-
" <tr>\n",
|
| 293 |
-
" <th>43</th>\n",
|
| 294 |
-
" <td>13gs</td>\n",
|
| 295 |
-
" <td>SAS</td>\n",
|
| 296 |
-
" <td>A</td>\n",
|
| 297 |
-
" <td>1</td>\n",
|
| 298 |
-
" <td>None</td>\n",
|
| 299 |
-
" <td>ki=24uM (SAS)</td>\n",
|
| 300 |
-
" <td>Ki=24uM (SAS)</td>\n",
|
| 301 |
-
" <td>None</td>\n",
|
| 302 |
-
" <td>MPPYTVVYFPVRGRCAALRMLLADQGQSWKEEVVTVETWQEGSLKA...</td>\n",
|
| 303 |
-
" <td>biolip/data/ligand/13gs_SAS_A_1.pdb</td>\n",
|
| 304 |
-
" <td>OC(=O)c1cc(/N=N/c2ccc(cc2)S(=O)(=O)Nc2ccccn2)c...</td>\n",
|
| 305 |
-
" <td>24.0000</td>\n",
|
| 306 |
-
" </tr>\n",
|
| 307 |
-
" <tr>\n",
|
| 308 |
-
" <th>53</th>\n",
|
| 309 |
-
" <td>16pk</td>\n",
|
| 310 |
-
" <td>BIS</td>\n",
|
| 311 |
-
" <td>A</td>\n",
|
| 312 |
-
" <td>1</td>\n",
|
| 313 |
-
" <td>None</td>\n",
|
| 314 |
-
" <td>None</td>\n",
|
| 315 |
-
" <td>Ki=6uM (BIS)</td>\n",
|
| 316 |
-
" <td>None</td>\n",
|
| 317 |
-
" <td>EKKSINECDLKGKKVLIRVDFNVPVKNGKITNDYRIRSALPTLKKV...</td>\n",
|
| 318 |
-
" <td>biolip/data/ligand/16pk_BIS_A_1.pdb</td>\n",
|
| 319 |
-
" <td>O[C@@H]1[C@@H](CO[P@](=O)(O[P@@](=O)(C(CCCC(P(...</td>\n",
|
| 320 |
-
" <td>6.0000</td>\n",
|
| 321 |
-
" </tr>\n",
|
| 322 |
-
" <tr>\n",
|
| 323 |
-
" <th>54</th>\n",
|
| 324 |
-
" <td>17gs</td>\n",
|
| 325 |
-
" <td>GTX</td>\n",
|
| 326 |
-
" <td>A</td>\n",
|
| 327 |
-
" <td>1</td>\n",
|
| 328 |
-
" <td>None</td>\n",
|
| 329 |
-
" <td>None</td>\n",
|
| 330 |
-
" <td>None</td>\n",
|
| 331 |
-
" <td>Kd=10000nM</td>\n",
|
| 332 |
-
" <td>MPPYTVVYFPVRGRCAALRMLLADQGQSWKEEVVTVETWQEGSLKA...</td>\n",
|
| 333 |
-
" <td>biolip/data/ligand/17gs_GTX_A_1.pdb</td>\n",
|
| 334 |
-
" <td>CCCCCCSC[C@@H](C(=O)NCC(=O)O)NC(=O)CC[C@@H](C(...</td>\n",
|
| 335 |
-
" <td>10.0000</td>\n",
|
| 336 |
-
" </tr>\n",
|
| 337 |
-
" <tr>\n",
|
| 338 |
-
" <th>55</th>\n",
|
| 339 |
-
" <td>181l</td>\n",
|
| 340 |
-
" <td>BNZ</td>\n",
|
| 341 |
-
" <td>A</td>\n",
|
| 342 |
-
" <td>1</td>\n",
|
| 343 |
-
" <td>None</td>\n",
|
| 344 |
-
" <td>Ka=5700M^-1 (BNZ)</td>\n",
|
| 345 |
-
" <td>None</td>\n",
|
| 346 |
-
" <td>Kd=175000nM</td>\n",
|
| 347 |
-
" <td>MNIFEMLRIDEGLRLKIYKDTEGYYTIGIGHLLTKSPSLNAAKSEL...</td>\n",
|
| 348 |
-
" <td>biolip/data/ligand/181l_BNZ_A_1.pdb</td>\n",
|
| 349 |
-
" <td>c1ccccc1</td>\n",
|
| 350 |
-
" <td>175.0000</td>\n",
|
| 351 |
-
" </tr>\n",
|
| 352 |
-
" <tr>\n",
|
| 353 |
-
" <th>...</th>\n",
|
| 354 |
-
" <td>...</td>\n",
|
| 355 |
-
" <td>...</td>\n",
|
| 356 |
-
" <td>...</td>\n",
|
| 357 |
-
" <td>...</td>\n",
|
| 358 |
-
" <td>...</td>\n",
|
| 359 |
-
" <td>...</td>\n",
|
| 360 |
-
" <td>...</td>\n",
|
| 361 |
-
" <td>...</td>\n",
|
| 362 |
-
" <td>...</td>\n",
|
| 363 |
-
" <td>...</td>\n",
|
| 364 |
-
" <td>...</td>\n",
|
| 365 |
-
" <td>...</td>\n",
|
| 366 |
-
" </tr>\n",
|
| 367 |
-
" <tr>\n",
|
| 368 |
-
" <th>105118</th>\n",
|
| 369 |
-
" <td>9hvp</td>\n",
|
| 370 |
-
" <td>0E9</td>\n",
|
| 371 |
-
" <td>A</td>\n",
|
| 372 |
-
" <td>1</td>\n",
|
| 373 |
-
" <td>None</td>\n",
|
| 374 |
-
" <td>None</td>\n",
|
| 375 |
-
" <td>Ki=4.5nM (5-mer)</td>\n",
|
| 376 |
-
" <td>None</td>\n",
|
| 377 |
-
" <td>PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMNLPGRWKPKM...</td>\n",
|
| 378 |
-
" <td>biolip/data/ligand/9hvp_0E9_A_1.pdb</td>\n",
|
| 379 |
-
" <td>O[C@@H]([C@H](Cc1ccccc1)NC(=O)[C@H](C(C)C)NC(=...</td>\n",
|
| 380 |
-
" <td>0.0045</td>\n",
|
| 381 |
-
" </tr>\n",
|
| 382 |
-
" <tr>\n",
|
| 383 |
-
" <th>105119</th>\n",
|
| 384 |
-
" <td>9hvp</td>\n",
|
| 385 |
-
" <td>0E9</td>\n",
|
| 386 |
-
" <td>A</td>\n",
|
| 387 |
-
" <td>1</td>\n",
|
| 388 |
-
" <td>None</td>\n",
|
| 389 |
-
" <td>None</td>\n",
|
| 390 |
-
" <td>Ki=4.5nM (5-mer)</td>\n",
|
| 391 |
-
" <td>None</td>\n",
|
| 392 |
-
" <td>PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMNLPGRWKPKM...</td>\n",
|
| 393 |
-
" <td>biolip/data/ligand/9hvp_0E9_A_1.pdb</td>\n",
|
| 394 |
-
" <td>O[C@@H]([C@H](Cc1ccccc1)NC(=O)[C@H](C(C)C)NC(=...</td>\n",
|
| 395 |
-
" <td>0.0045</td>\n",
|
| 396 |
-
" </tr>\n",
|
| 397 |
-
" <tr>\n",
|
| 398 |
-
" <th>105124</th>\n",
|
| 399 |
-
" <td>9icd</td>\n",
|
| 400 |
-
" <td>NAP</td>\n",
|
| 401 |
-
" <td>A</td>\n",
|
| 402 |
-
" <td>1</td>\n",
|
| 403 |
-
" <td>None</td>\n",
|
| 404 |
-
" <td>kd=125uM (NAP)</td>\n",
|
| 405 |
-
" <td>Kd=125uM (NAP)</td>\n",
|
| 406 |
-
" <td>None</td>\n",
|
| 407 |
-
" <td>SKVVVPAQGKKITLQNGKLNVPENPIIPYIEGDGIGVDVTPAMLKV...</td>\n",
|
| 408 |
-
" <td>biolip/data/ligand/9icd_NAP_A_1.pdb</td>\n",
|
| 409 |
-
" <td>O[C@@H]1[C@@H](COP(=O)(O)O)O[C@H]([C@@H]1OP(=O...</td>\n",
|
| 410 |
-
" <td>125.0000</td>\n",
|
| 411 |
-
" </tr>\n",
|
| 412 |
-
" <tr>\n",
|
| 413 |
-
" <th>105133</th>\n",
|
| 414 |
-
" <td>9lpr</td>\n",
|
| 415 |
-
" <td>III</td>\n",
|
| 416 |
-
" <td>P</td>\n",
|
| 417 |
-
" <td>1</td>\n",
|
| 418 |
-
" <td>None</td>\n",
|
| 419 |
-
" <td>None</td>\n",
|
| 420 |
-
" <td>Ki=2000nM (4-mer)</td>\n",
|
| 421 |
-
" <td>None</td>\n",
|
| 422 |
-
" <td>ANIVGGIEYSINNASLCSVGFSVTRGATKGFVTAGHCGTVNATARI...</td>\n",
|
| 423 |
-
" <td>biolip/data/ligand/9lpr_III_P_1.pdb</td>\n",
|
| 424 |
-
" <td>CC(C[C@@H](B(O)O)NC(=O)[C@@H]1CCCN1C(=O)[C@@H]...</td>\n",
|
| 425 |
-
" <td>2.0000</td>\n",
|
| 426 |
-
" </tr>\n",
|
| 427 |
-
" <tr>\n",
|
| 428 |
-
" <th>105138</th>\n",
|
| 429 |
-
" <td>9nse</td>\n",
|
| 430 |
-
" <td>ISU</td>\n",
|
| 431 |
-
" <td>B</td>\n",
|
| 432 |
-
" <td>2</td>\n",
|
| 433 |
-
" <td>None</td>\n",
|
| 434 |
-
" <td>Ki=0.039uM (ISU)</td>\n",
|
| 435 |
-
" <td>None</td>\n",
|
| 436 |
-
" <td>None</td>\n",
|
| 437 |
-
" <td>KFPRVKNWELGSITYDTLCAQSQQDGPCTPRRCLGSLVLPRKLQTR...</td>\n",
|
| 438 |
-
" <td>biolip/data/ligand/9nse_ISU_B_2.pdb</td>\n",
|
| 439 |
-
" <td>CC[Se]C(=N)N</td>\n",
|
| 440 |
-
" <td>0.0390</td>\n",
|
| 441 |
-
" </tr>\n",
|
| 442 |
-
" </tbody>\n",
|
| 443 |
-
"</table>\n",
|
| 444 |
-
"<p>12851 rows × 12 columns</p>\n",
|
| 445 |
-
"</div>"
|
| 446 |
-
],
|
| 447 |
-
"text/plain": [
|
| 448 |
-
" pdb chain l_id l_chain affinity_lit affinity_moad \\\n",
|
| 449 |
-
"38 11gs EAA A 1 None ki=1.5uM (GTT EAA) \n",
|
| 450 |
-
"43 13gs SAS A 1 None ki=24uM (SAS) \n",
|
| 451 |
-
"53 16pk BIS A 1 None None \n",
|
| 452 |
-
"54 17gs GTX A 1 None None \n",
|
| 453 |
-
"55 181l BNZ A 1 None Ka=5700M^-1 (BNZ) \n",
|
| 454 |
-
"... ... ... ... ... ... ... \n",
|
| 455 |
-
"105118 9hvp 0E9 A 1 None None \n",
|
| 456 |
-
"105119 9hvp 0E9 A 1 None None \n",
|
| 457 |
-
"105124 9icd NAP A 1 None kd=125uM (NAP) \n",
|
| 458 |
-
"105133 9lpr III P 1 None None \n",
|
| 459 |
-
"105138 9nse ISU B 2 None Ki=0.039uM (ISU) \n",
|
| 460 |
-
"\n",
|
| 461 |
-
" affinity_pdbbind-cn affinity_bindingdb \\\n",
|
| 462 |
-
"38 Ki=1.5uM (GTT-EAA) None \n",
|
| 463 |
-
"43 Ki=24uM (SAS) None \n",
|
| 464 |
-
"53 Ki=6uM (BIS) None \n",
|
| 465 |
-
"54 None Kd=10000nM \n",
|
| 466 |
-
"55 None Kd=175000nM \n",
|
| 467 |
-
"... ... ... \n",
|
| 468 |
-
"105118 Ki=4.5nM (5-mer) None \n",
|
| 469 |
-
"105119 Ki=4.5nM (5-mer) None \n",
|
| 470 |
-
"105124 Kd=125uM (NAP) None \n",
|
| 471 |
-
"105133 Ki=2000nM (4-mer) None \n",
|
| 472 |
-
"105138 None None \n",
|
| 473 |
-
"\n",
|
| 474 |
-
" seq \\\n",
|
| 475 |
-
"38 PYTVVYFPVRGRCAALRMLLADQGQSWKEEVVTVETWQEGSLKASC... \n",
|
| 476 |
-
"43 MPPYTVVYFPVRGRCAALRMLLADQGQSWKEEVVTVETWQEGSLKA... \n",
|
| 477 |
-
"53 EKKSINECDLKGKKVLIRVDFNVPVKNGKITNDYRIRSALPTLKKV... \n",
|
| 478 |
-
"54 MPPYTVVYFPVRGRCAALRMLLADQGQSWKEEVVTVETWQEGSLKA... \n",
|
| 479 |
-
"55 MNIFEMLRIDEGLRLKIYKDTEGYYTIGIGHLLTKSPSLNAAKSEL... \n",
|
| 480 |
-
"... ... \n",
|
| 481 |
-
"105118 PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMNLPGRWKPKM... \n",
|
| 482 |
-
"105119 PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMNLPGRWKPKM... \n",
|
| 483 |
-
"105124 SKVVVPAQGKKITLQNGKLNVPENPIIPYIEGDGIGVDVTPAMLKV... \n",
|
| 484 |
-
"105133 ANIVGGIEYSINNASLCSVGFSVTRGATKGFVTAGHCGTVNATARI... \n",
|
| 485 |
-
"105138 KFPRVKNWELGSITYDTLCAQSQQDGPCTPRRCLGSLVLPRKLQTR... \n",
|
| 486 |
-
"\n",
|
| 487 |
-
" ligand_fn \\\n",
|
| 488 |
-
"38 biolip/data/ligand/11gs_EAA_A_1.pdb \n",
|
| 489 |
-
"43 biolip/data/ligand/13gs_SAS_A_1.pdb \n",
|
| 490 |
-
"53 biolip/data/ligand/16pk_BIS_A_1.pdb \n",
|
| 491 |
-
"54 biolip/data/ligand/17gs_GTX_A_1.pdb \n",
|
| 492 |
-
"55 biolip/data/ligand/181l_BNZ_A_1.pdb \n",
|
| 493 |
-
"... ... \n",
|
| 494 |
-
"105118 biolip/data/ligand/9hvp_0E9_A_1.pdb \n",
|
| 495 |
-
"105119 biolip/data/ligand/9hvp_0E9_A_1.pdb \n",
|
| 496 |
-
"105124 biolip/data/ligand/9icd_NAP_A_1.pdb \n",
|
| 497 |
-
"105133 biolip/data/ligand/9lpr_III_P_1.pdb \n",
|
| 498 |
-
"105138 biolip/data/ligand/9nse_ISU_B_2.pdb \n",
|
| 499 |
-
"\n",
|
| 500 |
-
" smiles affinity_uM \n",
|
| 501 |
-
"38 CC[C@H](C(=O)c1ccc(c(c1Cl)Cl)OCC(=O)O)C 1.5000 \n",
|
| 502 |
-
"43 OC(=O)c1cc(/N=N/c2ccc(cc2)S(=O)(=O)Nc2ccccn2)c... 24.0000 \n",
|
| 503 |
-
"53 O[C@@H]1[C@@H](CO[P@](=O)(O[P@@](=O)(C(CCCC(P(... 6.0000 \n",
|
| 504 |
-
"54 CCCCCCSC[C@@H](C(=O)NCC(=O)O)NC(=O)CC[C@@H](C(... 10.0000 \n",
|
| 505 |
-
"55 c1ccccc1 175.0000 \n",
|
| 506 |
-
"... ... ... \n",
|
| 507 |
-
"105118 O[C@@H]([C@H](Cc1ccccc1)NC(=O)[C@H](C(C)C)NC(=... 0.0045 \n",
|
| 508 |
-
"105119 O[C@@H]([C@H](Cc1ccccc1)NC(=O)[C@H](C(C)C)NC(=... 0.0045 \n",
|
| 509 |
-
"105124 O[C@@H]1[C@@H](COP(=O)(O)O)O[C@H]([C@@H]1OP(=O... 125.0000 \n",
|
| 510 |
-
"105133 CC(C[C@@H](B(O)O)NC(=O)[C@@H]1CCCN1C(=O)[C@@H]... 2.0000 \n",
|
| 511 |
-
"105138 CC[Se]C(=N)N 0.0390 \n",
|
| 512 |
-
"\n",
|
| 513 |
-
"[12851 rows x 12 columns]"
|
| 514 |
-
]
|
| 515 |
-
},
|
| 516 |
-
"execution_count": 61,
|
| 517 |
-
"metadata": {},
|
| 518 |
-
"output_type": "execute_result"
|
| 519 |
-
}
|
| 520 |
-
],
|
| 521 |
-
"source": [
|
| 522 |
-
"df_affinity[~df_affinity['affinity_uM'].isnull()]"
|
| 523 |
-
]
|
| 524 |
-
},
|
| 525 |
-
{
|
| 526 |
-
"cell_type": "code",
|
| 527 |
-
"execution_count": 63,
|
| 528 |
-
"id": "2b483565-3c99-4c42-b2a9-f7b97cd8e80e",
|
| 529 |
-
"metadata": {},
|
| 530 |
-
"outputs": [],
|
| 531 |
-
"source": [
|
| 532 |
-
"df_affinity.to_parquet('data/biolip.parquet')"
|
| 533 |
-
]
|
| 534 |
-
},
|
| 535 |
-
{
|
| 536 |
-
"cell_type": "code",
|
| 537 |
-
"execution_count": 64,
|
| 538 |
-
"id": "68dd5e45-b31d-492d-a47e-39072b67fa72",
|
| 539 |
-
"metadata": {},
|
| 540 |
-
"outputs": [
|
| 541 |
-
{
|
| 542 |
-
"data": {
|
| 543 |
-
"text/plain": [
|
| 544 |
-
"13645"
|
| 545 |
-
]
|
| 546 |
-
},
|
| 547 |
-
"execution_count": 64,
|
| 548 |
-
"metadata": {},
|
| 549 |
-
"output_type": "execute_result"
|
| 550 |
-
}
|
| 551 |
-
],
|
| 552 |
-
"source": [
|
| 553 |
-
"len(df_affinity)"
|
| 554 |
-
]
|
| 555 |
-
},
|
| 556 |
-
{
|
| 557 |
-
"cell_type": "code",
|
| 558 |
-
"execution_count": null,
|
| 559 |
-
"id": "cf11317d-bbab-40f1-a8a2-b6fd6126e998",
|
| 560 |
-
"metadata": {},
|
| 561 |
-
"outputs": [],
|
| 562 |
-
"source": []
|
| 563 |
-
}
|
| 564 |
-
],
|
| 565 |
-
"metadata": {
|
| 566 |
-
"kernelspec": {
|
| 567 |
-
"display_name": "Python 3",
|
| 568 |
-
"language": "python",
|
| 569 |
-
"name": "python3"
|
| 570 |
-
},
|
| 571 |
-
"language_info": {
|
| 572 |
-
"codemirror_mode": {
|
| 573 |
-
"name": "ipython",
|
| 574 |
-
"version": 3
|
| 575 |
-
},
|
| 576 |
-
"file_extension": ".py",
|
| 577 |
-
"mimetype": "text/x-python",
|
| 578 |
-
"name": "python",
|
| 579 |
-
"nbconvert_exporter": "python",
|
| 580 |
-
"pygments_lexer": "ipython3",
|
| 581 |
-
"version": "3.9.4"
|
| 582 |
-
}
|
| 583 |
-
},
|
| 584 |
-
"nbformat": 4,
|
| 585 |
-
"nbformat_minor": 5
|
| 586 |
-
}
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biolip.py
DELETED
|
@@ -1,41 +0,0 @@
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|
| 1 |
-
from mpi4py import MPI
|
| 2 |
-
from mpi4py.futures import MPICommExecutor
|
| 3 |
-
|
| 4 |
-
from openbabel import pybel
|
| 5 |
-
from Bio.PDB import *
|
| 6 |
-
parser = PDBParser()
|
| 7 |
-
|
| 8 |
-
import os
|
| 9 |
-
molecular_weight_cutoff = 2500
|
| 10 |
-
def parse_ligand(fn):
|
| 11 |
-
print(fn)
|
| 12 |
-
try:
|
| 13 |
-
struct = parser.get_structure('lig',fn)
|
| 14 |
-
if len(list(struct.get_atoms())) > molecular_weight_cutoff:
|
| 15 |
-
raise ValueError
|
| 16 |
-
mol = next(pybel.readfile('pdb',fn))
|
| 17 |
-
if mol.molwt > molecular_weight_cutoff:
|
| 18 |
-
raise ValueError
|
| 19 |
-
smi = mol.write('can').split('\t')[0]
|
| 20 |
-
return smi
|
| 21 |
-
except:
|
| 22 |
-
return None
|
| 23 |
-
|
| 24 |
-
|
| 25 |
-
if __name__ == '__main__':
|
| 26 |
-
import glob
|
| 27 |
-
|
| 28 |
-
comm = MPI.COMM_WORLD
|
| 29 |
-
with MPICommExecutor(comm, root=0) as executor:
|
| 30 |
-
if executor is not None:
|
| 31 |
-
import pandas as pd
|
| 32 |
-
|
| 33 |
-
df = pd.read_table('biolip/data/BioLiP_2013-03-6_nr.txt',sep='\t',header=None,usecols=[0,4,5,6,13,14,15,16,19])
|
| 34 |
-
df = df.rename(columns={0:'pdb',4:'chain',5:'l_id',6:'l_chain',
|
| 35 |
-
13: 'affinity_lit',14: 'affinity_moad',15: 'affinity_pdbbind-cn',16:'affinity_bindingdb',
|
| 36 |
-
19: 'seq'})
|
| 37 |
-
base = 'biolip/data/ligand/'
|
| 38 |
-
df['ligand_fn'] = base + df['pdb']+'_'+df['chain']+'_'+df['l_id'].astype(str)+'_'+df['l_chain'].astype(str)+'.pdb'
|
| 39 |
-
smiles = list(executor.map(parse_ligand, df['ligand_fn']))
|
| 40 |
-
df['smiles'] = smiles
|
| 41 |
-
df.to_parquet('data/biolip_complex.parquet')
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biolip.slurm
DELETED
|
@@ -1,10 +0,0 @@
|
|
| 1 |
-
#!/bin/bash
|
| 2 |
-
#SBATCH -J preprocess_biolip
|
| 3 |
-
#SBATCH -p batch
|
| 4 |
-
#SBATCH -A BIP214
|
| 5 |
-
#SBATCH -t 3:00:00
|
| 6 |
-
#SBATCH -N 11
|
| 7 |
-
#SBATCH --ntasks-per-node=32
|
| 8 |
-
|
| 9 |
-
export PYTHONUNBUFFERED=1
|
| 10 |
-
srun python biolip.py
|
|
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|
combine_dbs.ipynb
DELETED
|
@@ -1,1763 +0,0 @@
|
|
| 1 |
-
{
|
| 2 |
-
"cells": [
|
| 3 |
-
{
|
| 4 |
-
"cell_type": "code",
|
| 5 |
-
"execution_count": 2,
|
| 6 |
-
"id": "95bd761a-fe51-4a8e-bc70-1365260ba5f8",
|
| 7 |
-
"metadata": {},
|
| 8 |
-
"outputs": [],
|
| 9 |
-
"source": [
|
| 10 |
-
"import pandas as pd"
|
| 11 |
-
]
|
| 12 |
-
},
|
| 13 |
-
{
|
| 14 |
-
"cell_type": "code",
|
| 15 |
-
"execution_count": 2,
|
| 16 |
-
"id": "b0859483-5e19-4280-9f53-0d00a6f22d34",
|
| 17 |
-
"metadata": {},
|
| 18 |
-
"outputs": [],
|
| 19 |
-
"source": [
|
| 20 |
-
"df_pdbbind = pd.read_parquet('data/pdbbind.parquet')\n",
|
| 21 |
-
"df_pdbbind = df_pdbbind[['seq','smiles','affinity_uM']]"
|
| 22 |
-
]
|
| 23 |
-
},
|
| 24 |
-
{
|
| 25 |
-
"cell_type": "code",
|
| 26 |
-
"execution_count": 3,
|
| 27 |
-
"id": "f30732b7-7444-47ad-84e7-566e7a6f2f8e",
|
| 28 |
-
"metadata": {},
|
| 29 |
-
"outputs": [
|
| 30 |
-
{
|
| 31 |
-
"data": {
|
| 32 |
-
"text/html": [
|
| 33 |
-
"<div>\n",
|
| 34 |
-
"<style scoped>\n",
|
| 35 |
-
" .dataframe tbody tr th:only-of-type {\n",
|
| 36 |
-
" vertical-align: middle;\n",
|
| 37 |
-
" }\n",
|
| 38 |
-
"\n",
|
| 39 |
-
" .dataframe tbody tr th {\n",
|
| 40 |
-
" vertical-align: top;\n",
|
| 41 |
-
" }\n",
|
| 42 |
-
"\n",
|
| 43 |
-
" .dataframe thead th {\n",
|
| 44 |
-
" text-align: right;\n",
|
| 45 |
-
" }\n",
|
| 46 |
-
"</style>\n",
|
| 47 |
-
"<table border=\"1\" class=\"dataframe\">\n",
|
| 48 |
-
" <thead>\n",
|
| 49 |
-
" <tr style=\"text-align: right;\">\n",
|
| 50 |
-
" <th></th>\n",
|
| 51 |
-
" <th>seq</th>\n",
|
| 52 |
-
" <th>smiles</th>\n",
|
| 53 |
-
" <th>affinity_uM</th>\n",
|
| 54 |
-
" </tr>\n",
|
| 55 |
-
" </thead>\n",
|
| 56 |
-
" <tbody>\n",
|
| 57 |
-
" <tr>\n",
|
| 58 |
-
" <th>0</th>\n",
|
| 59 |
-
" <td>MTVPDRSEIAGKWYVVALASNTEFFLREKDKMKMAMARISFLGEDE...</td>\n",
|
| 60 |
-
" <td>CCCCCCCCCCCCCCCCCCCC(=O)O</td>\n",
|
| 61 |
-
" <td>0.026</td>\n",
|
| 62 |
-
" </tr>\n",
|
| 63 |
-
" <tr>\n",
|
| 64 |
-
" <th>1</th>\n",
|
| 65 |
-
" <td>APQTITELCSEYRNTQIYTINDKILSYTESMAGKREMVIITFKSGE...</td>\n",
|
| 66 |
-
" <td>OC[C@H]1O[C@H](Oc2cccc(c2)N(=O)=O)[C@@H]([C@H]...</td>\n",
|
| 67 |
-
" <td>500.000</td>\n",
|
| 68 |
-
" </tr>\n",
|
| 69 |
-
" <tr>\n",
|
| 70 |
-
" <th>2</th>\n",
|
| 71 |
-
" <td>VETFAFQAEIAQLMSLIINTFYSNKEIFLRELISNSSDALDKIRYE...</td>\n",
|
| 72 |
-
" <td>COc1ccc(cc1)c1c(onc1c1cc(C(C)C)c(cc1O)O)NC(=O)...</td>\n",
|
| 73 |
-
" <td>0.023</td>\n",
|
| 74 |
-
" </tr>\n",
|
| 75 |
-
" <tr>\n",
|
| 76 |
-
" <th>3</th>\n",
|
| 77 |
-
" <td>AAPFDKSKNVAQSIDQLIGQTPALYLNKLNNTKAKVVLKMECENPM...</td>\n",
|
| 78 |
-
" <td>OC[C@@H](C(=O)N[C@@H]([C@H](CC)C)C(=O)O)NC(=O)...</td>\n",
|
| 79 |
-
" <td>6.430</td>\n",
|
| 80 |
-
" </tr>\n",
|
| 81 |
-
" <tr>\n",
|
| 82 |
-
" <th>4</th>\n",
|
| 83 |
-
" <td>YITFRSFTAVLIAFFLTLVLSPSFINRLRKIQRKKYTPTMGGIVIL...</td>\n",
|
| 84 |
-
" <td>CO[C@@H]1[C@H](O[C@H]([C@@H]1O)n1ccc(=O)[nH]c1...</td>\n",
|
| 85 |
-
" <td>0.185</td>\n",
|
| 86 |
-
" </tr>\n",
|
| 87 |
-
" </tbody>\n",
|
| 88 |
-
"</table>\n",
|
| 89 |
-
"</div>"
|
| 90 |
-
],
|
| 91 |
-
"text/plain": [
|
| 92 |
-
" seq \\\n",
|
| 93 |
-
"0 MTVPDRSEIAGKWYVVALASNTEFFLREKDKMKMAMARISFLGEDE... \n",
|
| 94 |
-
"1 APQTITELCSEYRNTQIYTINDKILSYTESMAGKREMVIITFKSGE... \n",
|
| 95 |
-
"2 VETFAFQAEIAQLMSLIINTFYSNKEIFLRELISNSSDALDKIRYE... \n",
|
| 96 |
-
"3 AAPFDKSKNVAQSIDQLIGQTPALYLNKLNNTKAKVVLKMECENPM... \n",
|
| 97 |
-
"4 YITFRSFTAVLIAFFLTLVLSPSFINRLRKIQRKKYTPTMGGIVIL... \n",
|
| 98 |
-
"\n",
|
| 99 |
-
" smiles affinity_uM \n",
|
| 100 |
-
"0 CCCCCCCCCCCCCCCCCCCC(=O)O 0.026 \n",
|
| 101 |
-
"1 OC[C@H]1O[C@H](Oc2cccc(c2)N(=O)=O)[C@@H]([C@H]... 500.000 \n",
|
| 102 |
-
"2 COc1ccc(cc1)c1c(onc1c1cc(C(C)C)c(cc1O)O)NC(=O)... 0.023 \n",
|
| 103 |
-
"3 OC[C@@H](C(=O)N[C@@H]([C@H](CC)C)C(=O)O)NC(=O)... 6.430 \n",
|
| 104 |
-
"4 CO[C@@H]1[C@H](O[C@H]([C@@H]1O)n1ccc(=O)[nH]c1... 0.185 "
|
| 105 |
-
]
|
| 106 |
-
},
|
| 107 |
-
"execution_count": 3,
|
| 108 |
-
"metadata": {},
|
| 109 |
-
"output_type": "execute_result"
|
| 110 |
-
}
|
| 111 |
-
],
|
| 112 |
-
"source": [
|
| 113 |
-
"df_pdbbind.head()"
|
| 114 |
-
]
|
| 115 |
-
},
|
| 116 |
-
{
|
| 117 |
-
"cell_type": "code",
|
| 118 |
-
"execution_count": 4,
|
| 119 |
-
"id": "2787b9fd-3d6f-4ae3-a3ad-d3539b72782b",
|
| 120 |
-
"metadata": {},
|
| 121 |
-
"outputs": [],
|
| 122 |
-
"source": [
|
| 123 |
-
"from rdkit import Chem\n",
|
| 124 |
-
"from rdkit.Chem import MACCSkeys\n",
|
| 125 |
-
"import numpy as np\n",
|
| 126 |
-
"\n",
|
| 127 |
-
"def get_maccs(smi):\n",
|
| 128 |
-
" try:\n",
|
| 129 |
-
" mol = Chem.MolFromSmiles(smi)\n",
|
| 130 |
-
" arr = np.packbits([0 if c=='0' else 1 for c in MACCSkeys.GenMACCSKeys(mol).ToBitString()])\n",
|
| 131 |
-
" return np.pad(arr,(0,3)).view(np.uint32)\n",
|
| 132 |
-
" except Exception:\n",
|
| 133 |
-
" pass"
|
| 134 |
-
]
|
| 135 |
-
},
|
| 136 |
-
{
|
| 137 |
-
"cell_type": "code",
|
| 138 |
-
"execution_count": 5,
|
| 139 |
-
"id": "d1abe1c8-ac66-4289-8964-367a5b18528d",
|
| 140 |
-
"metadata": {},
|
| 141 |
-
"outputs": [],
|
| 142 |
-
"source": [
|
| 143 |
-
"df_bindingdb = pd.read_parquet('data/bindingdb.parquet')\n",
|
| 144 |
-
"df_bindingdb = df_bindingdb[['seq','Ligand SMILES','affinity_uM']].rename(columns={'Ligand SMILES': 'smiles'})"
|
| 145 |
-
]
|
| 146 |
-
},
|
| 147 |
-
{
|
| 148 |
-
"cell_type": "code",
|
| 149 |
-
"execution_count": 6,
|
| 150 |
-
"id": "988bab9c-5147-44e2-92ef-902eaf3c5a90",
|
| 151 |
-
"metadata": {},
|
| 152 |
-
"outputs": [
|
| 153 |
-
{
|
| 154 |
-
"data": {
|
| 155 |
-
"text/html": [
|
| 156 |
-
"<div>\n",
|
| 157 |
-
"<style scoped>\n",
|
| 158 |
-
" .dataframe tbody tr th:only-of-type {\n",
|
| 159 |
-
" vertical-align: middle;\n",
|
| 160 |
-
" }\n",
|
| 161 |
-
"\n",
|
| 162 |
-
" .dataframe tbody tr th {\n",
|
| 163 |
-
" vertical-align: top;\n",
|
| 164 |
-
" }\n",
|
| 165 |
-
"\n",
|
| 166 |
-
" .dataframe thead th {\n",
|
| 167 |
-
" text-align: right;\n",
|
| 168 |
-
" }\n",
|
| 169 |
-
"</style>\n",
|
| 170 |
-
"<table border=\"1\" class=\"dataframe\">\n",
|
| 171 |
-
" <thead>\n",
|
| 172 |
-
" <tr style=\"text-align: right;\">\n",
|
| 173 |
-
" <th></th>\n",
|
| 174 |
-
" <th>seq</th>\n",
|
| 175 |
-
" <th>smiles</th>\n",
|
| 176 |
-
" <th>affinity_uM</th>\n",
|
| 177 |
-
" </tr>\n",
|
| 178 |
-
" </thead>\n",
|
| 179 |
-
" <tbody>\n",
|
| 180 |
-
" <tr>\n",
|
| 181 |
-
" <th>0</th>\n",
|
| 182 |
-
" <td>PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKM...</td>\n",
|
| 183 |
-
" <td>COc1cc2c(Nc3ccc(Br)cc3F)ncnc2cc1OCC1CCN(C)CC1</td>\n",
|
| 184 |
-
" <td>0.00024</td>\n",
|
| 185 |
-
" </tr>\n",
|
| 186 |
-
" <tr>\n",
|
| 187 |
-
" <th>1</th>\n",
|
| 188 |
-
" <td>PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKM...</td>\n",
|
| 189 |
-
" <td>O[C@@H]1[C@@H](O)[C@@H](Cc2ccccc2)N(C\\C=C\\c2cn...</td>\n",
|
| 190 |
-
" <td>0.00025</td>\n",
|
| 191 |
-
" </tr>\n",
|
| 192 |
-
" <tr>\n",
|
| 193 |
-
" <th>2</th>\n",
|
| 194 |
-
" <td>PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKM...</td>\n",
|
| 195 |
-
" <td>O[C@@H]1[C@@H](O)[C@@H](Cc2ccccc2)N(CC2CC2)C(=...</td>\n",
|
| 196 |
-
" <td>0.00041</td>\n",
|
| 197 |
-
" </tr>\n",
|
| 198 |
-
" <tr>\n",
|
| 199 |
-
" <th>3</th>\n",
|
| 200 |
-
" <td>PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKM...</td>\n",
|
| 201 |
-
" <td>OCCCCCCN1[C@H](Cc2ccccc2)[C@H](O)[C@@H](O)[C@@...</td>\n",
|
| 202 |
-
" <td>0.00080</td>\n",
|
| 203 |
-
" </tr>\n",
|
| 204 |
-
" <tr>\n",
|
| 205 |
-
" <th>4</th>\n",
|
| 206 |
-
" <td>PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKM...</td>\n",
|
| 207 |
-
" <td>OCCCCCN1[C@H](Cc2ccccc2)[C@H](O)[C@@H](O)[C@@H...</td>\n",
|
| 208 |
-
" <td>0.00099</td>\n",
|
| 209 |
-
" </tr>\n",
|
| 210 |
-
" </tbody>\n",
|
| 211 |
-
"</table>\n",
|
| 212 |
-
"</div>"
|
| 213 |
-
],
|
| 214 |
-
"text/plain": [
|
| 215 |
-
" seq \\\n",
|
| 216 |
-
"0 PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKM... \n",
|
| 217 |
-
"1 PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKM... \n",
|
| 218 |
-
"2 PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKM... \n",
|
| 219 |
-
"3 PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKM... \n",
|
| 220 |
-
"4 PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKM... \n",
|
| 221 |
-
"\n",
|
| 222 |
-
" smiles affinity_uM \n",
|
| 223 |
-
"0 COc1cc2c(Nc3ccc(Br)cc3F)ncnc2cc1OCC1CCN(C)CC1 0.00024 \n",
|
| 224 |
-
"1 O[C@@H]1[C@@H](O)[C@@H](Cc2ccccc2)N(C\\C=C\\c2cn... 0.00025 \n",
|
| 225 |
-
"2 O[C@@H]1[C@@H](O)[C@@H](Cc2ccccc2)N(CC2CC2)C(=... 0.00041 \n",
|
| 226 |
-
"3 OCCCCCCN1[C@H](Cc2ccccc2)[C@H](O)[C@@H](O)[C@@... 0.00080 \n",
|
| 227 |
-
"4 OCCCCCN1[C@H](Cc2ccccc2)[C@H](O)[C@@H](O)[C@@H... 0.00099 "
|
| 228 |
-
]
|
| 229 |
-
},
|
| 230 |
-
"execution_count": 6,
|
| 231 |
-
"metadata": {},
|
| 232 |
-
"output_type": "execute_result"
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| 233 |
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}
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| 234 |
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],
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| 235 |
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"source": [
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| 236 |
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"df_bindingdb.head()"
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| 237 |
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| 238 |
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},
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| 239 |
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{
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| 240 |
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"cell_type": "code",
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| 241 |
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"execution_count": 7,
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| 242 |
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"id": "d7bfee2a-c4e6-48c9-b0c6-52f6a69c7453",
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| 243 |
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"metadata": {},
|
| 244 |
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"outputs": [],
|
| 245 |
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"source": [
|
| 246 |
-
"df_moad = pd.read_parquet('data/moad.parquet')\n",
|
| 247 |
-
"df_moad = df_moad[['seq','smiles','affinity_uM']]"
|
| 248 |
-
]
|
| 249 |
-
},
|
| 250 |
-
{
|
| 251 |
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"cell_type": "code",
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| 252 |
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"execution_count": 8,
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| 253 |
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"id": "25553199-1715-40fb-9260-427bdd6c3706",
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| 254 |
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"metadata": {},
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| 255 |
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"outputs": [
|
| 256 |
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{
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| 257 |
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"data": {
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| 258 |
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| 259 |
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| 260 |
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| 273 |
-
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|
| 274 |
-
" <thead>\n",
|
| 275 |
-
" <tr style=\"text-align: right;\">\n",
|
| 276 |
-
" <th></th>\n",
|
| 277 |
-
" <th>seq</th>\n",
|
| 278 |
-
" <th>smiles</th>\n",
|
| 279 |
-
" <th>affinity_uM</th>\n",
|
| 280 |
-
" </tr>\n",
|
| 281 |
-
" </thead>\n",
|
| 282 |
-
" <tbody>\n",
|
| 283 |
-
" <tr>\n",
|
| 284 |
-
" <th>0</th>\n",
|
| 285 |
-
" <td>NYIVPGEYRVAEGEIEINAGREKTTIRVSNTGDRPIQVGSHIHFVE...</td>\n",
|
| 286 |
-
" <td>NP(=O)(N)O</td>\n",
|
| 287 |
-
" <td>0.000620</td>\n",
|
| 288 |
-
" </tr>\n",
|
| 289 |
-
" <tr>\n",
|
| 290 |
-
" <th>1</th>\n",
|
| 291 |
-
" <td>NYIVPGEYRVAEGEIEINAGREKTTIRVSNTGDRPIQVGSHIHFVE...</td>\n",
|
| 292 |
-
" <td>CC(=O)NO</td>\n",
|
| 293 |
-
" <td>2.600000</td>\n",
|
| 294 |
-
" </tr>\n",
|
| 295 |
-
" <tr>\n",
|
| 296 |
-
" <th>2</th>\n",
|
| 297 |
-
" <td>MEGMRRPTPTVYVGRVPIGGAHPIAVQSMTNTPTRDVEATTAQVLE...</td>\n",
|
| 298 |
-
" <td>C#CCCOP(=O)(O)OP(=O)(O)O</td>\n",
|
| 299 |
-
" <td>0.580000</td>\n",
|
| 300 |
-
" </tr>\n",
|
| 301 |
-
" <tr>\n",
|
| 302 |
-
" <th>3</th>\n",
|
| 303 |
-
" <td>MEGMRRPTPTVYVGRVPIGGAHPIAVQSMTNTPTRDVEATTAQVLE...</td>\n",
|
| 304 |
-
" <td>C#CCOP(=O)(O)OP(=O)(O)O</td>\n",
|
| 305 |
-
" <td>0.770000</td>\n",
|
| 306 |
-
" </tr>\n",
|
| 307 |
-
" <tr>\n",
|
| 308 |
-
" <th>4</th>\n",
|
| 309 |
-
" <td>MTDMSIKFELIDVPIPQGTNVIIGQAHFIKTVEDLYEALVTSVPGV...</td>\n",
|
| 310 |
-
" <td>c1nc(c2c(n1)n(cn2)[C@H]3[C@@H]([C@@H]([C@H](O3...</td>\n",
|
| 311 |
-
" <td>15.000000</td>\n",
|
| 312 |
-
" </tr>\n",
|
| 313 |
-
" <tr>\n",
|
| 314 |
-
" <th>...</th>\n",
|
| 315 |
-
" <td>...</td>\n",
|
| 316 |
-
" <td>...</td>\n",
|
| 317 |
-
" <td>...</td>\n",
|
| 318 |
-
" </tr>\n",
|
| 319 |
-
" <tr>\n",
|
| 320 |
-
" <th>25420</th>\n",
|
| 321 |
-
" <td>MGSSHHHHHHSSGLVPRGSHMASNPSLIRSESWQVYEGNEANLLDG...</td>\n",
|
| 322 |
-
" <td>None</td>\n",
|
| 323 |
-
" <td>127.226463</td>\n",
|
| 324 |
-
" </tr>\n",
|
| 325 |
-
" <tr>\n",
|
| 326 |
-
" <th>25421</th>\n",
|
| 327 |
-
" <td>MGSSHHHHHHSSGLVPRGSHMASNPSLIRSESWQVYEGNEANLLDG...</td>\n",
|
| 328 |
-
" <td>None</td>\n",
|
| 329 |
-
" <td>127.226463</td>\n",
|
| 330 |
-
" </tr>\n",
|
| 331 |
-
" <tr>\n",
|
| 332 |
-
" <th>25422</th>\n",
|
| 333 |
-
" <td>MGSSHHHHHHSSGLVPRGSHMASNPSLIRSESWQVYEGNEANLLDG...</td>\n",
|
| 334 |
-
" <td>None</td>\n",
|
| 335 |
-
" <td>169.204738</td>\n",
|
| 336 |
-
" </tr>\n",
|
| 337 |
-
" <tr>\n",
|
| 338 |
-
" <th>25423</th>\n",
|
| 339 |
-
" <td>MGSSHHHHHHSSGLVPRGSHMASNPSLIRSESWQVYEGNEANLLDG...</td>\n",
|
| 340 |
-
" <td>None</td>\n",
|
| 341 |
-
" <td>169.204738</td>\n",
|
| 342 |
-
" </tr>\n",
|
| 343 |
-
" <tr>\n",
|
| 344 |
-
" <th>25424</th>\n",
|
| 345 |
-
" <td>MGSSHHHHHHSSGLVPRGSHMASNPSLIRSESWQVYEGNEANLLDG...</td>\n",
|
| 346 |
-
" <td>None</td>\n",
|
| 347 |
-
" <td>169.204738</td>\n",
|
| 348 |
-
" </tr>\n",
|
| 349 |
-
" </tbody>\n",
|
| 350 |
-
"</table>\n",
|
| 351 |
-
"<p>25425 rows × 3 columns</p>\n",
|
| 352 |
-
"</div>"
|
| 353 |
-
],
|
| 354 |
-
"text/plain": [
|
| 355 |
-
" seq \\\n",
|
| 356 |
-
"0 NYIVPGEYRVAEGEIEINAGREKTTIRVSNTGDRPIQVGSHIHFVE... \n",
|
| 357 |
-
"1 NYIVPGEYRVAEGEIEINAGREKTTIRVSNTGDRPIQVGSHIHFVE... \n",
|
| 358 |
-
"2 MEGMRRPTPTVYVGRVPIGGAHPIAVQSMTNTPTRDVEATTAQVLE... \n",
|
| 359 |
-
"3 MEGMRRPTPTVYVGRVPIGGAHPIAVQSMTNTPTRDVEATTAQVLE... \n",
|
| 360 |
-
"4 MTDMSIKFELIDVPIPQGTNVIIGQAHFIKTVEDLYEALVTSVPGV... \n",
|
| 361 |
-
"... ... \n",
|
| 362 |
-
"25420 MGSSHHHHHHSSGLVPRGSHMASNPSLIRSESWQVYEGNEANLLDG... \n",
|
| 363 |
-
"25421 MGSSHHHHHHSSGLVPRGSHMASNPSLIRSESWQVYEGNEANLLDG... \n",
|
| 364 |
-
"25422 MGSSHHHHHHSSGLVPRGSHMASNPSLIRSESWQVYEGNEANLLDG... \n",
|
| 365 |
-
"25423 MGSSHHHHHHSSGLVPRGSHMASNPSLIRSESWQVYEGNEANLLDG... \n",
|
| 366 |
-
"25424 MGSSHHHHHHSSGLVPRGSHMASNPSLIRSESWQVYEGNEANLLDG... \n",
|
| 367 |
-
"\n",
|
| 368 |
-
" smiles affinity_uM \n",
|
| 369 |
-
"0 NP(=O)(N)O 0.000620 \n",
|
| 370 |
-
"1 CC(=O)NO 2.600000 \n",
|
| 371 |
-
"2 C#CCCOP(=O)(O)OP(=O)(O)O 0.580000 \n",
|
| 372 |
-
"3 C#CCOP(=O)(O)OP(=O)(O)O 0.770000 \n",
|
| 373 |
-
"4 c1nc(c2c(n1)n(cn2)[C@H]3[C@@H]([C@@H]([C@H](O3... 15.000000 \n",
|
| 374 |
-
"... ... ... \n",
|
| 375 |
-
"25420 None 127.226463 \n",
|
| 376 |
-
"25421 None 127.226463 \n",
|
| 377 |
-
"25422 None 169.204738 \n",
|
| 378 |
-
"25423 None 169.204738 \n",
|
| 379 |
-
"25424 None 169.204738 \n",
|
| 380 |
-
"\n",
|
| 381 |
-
"[25425 rows x 3 columns]"
|
| 382 |
-
]
|
| 383 |
-
},
|
| 384 |
-
"execution_count": 8,
|
| 385 |
-
"metadata": {},
|
| 386 |
-
"output_type": "execute_result"
|
| 387 |
-
}
|
| 388 |
-
],
|
| 389 |
-
"source": [
|
| 390 |
-
"df_moad"
|
| 391 |
-
]
|
| 392 |
-
},
|
| 393 |
-
{
|
| 394 |
-
"cell_type": "code",
|
| 395 |
-
"execution_count": 9,
|
| 396 |
-
"id": "b2c936bc-cdc8-4bc1-b92d-f8755fd65f0a",
|
| 397 |
-
"metadata": {},
|
| 398 |
-
"outputs": [],
|
| 399 |
-
"source": [
|
| 400 |
-
"df_biolip = pd.read_parquet('data/biolip.parquet')\n",
|
| 401 |
-
"df_biolip = df_biolip[['seq','smiles','affinity_uM']]"
|
| 402 |
-
]
|
| 403 |
-
},
|
| 404 |
-
{
|
| 405 |
-
"cell_type": "code",
|
| 406 |
-
"execution_count": 10,
|
| 407 |
-
"id": "cee93018-601d-458b-af44-bd978da7a2bc",
|
| 408 |
-
"metadata": {},
|
| 409 |
-
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|
| 410 |
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|
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|
| 425 |
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| 426 |
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|
| 427 |
-
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|
| 428 |
-
" <thead>\n",
|
| 429 |
-
" <tr style=\"text-align: right;\">\n",
|
| 430 |
-
" <th></th>\n",
|
| 431 |
-
" <th>seq</th>\n",
|
| 432 |
-
" <th>smiles</th>\n",
|
| 433 |
-
" <th>affinity_uM</th>\n",
|
| 434 |
-
" </tr>\n",
|
| 435 |
-
" </thead>\n",
|
| 436 |
-
" <tbody>\n",
|
| 437 |
-
" <tr>\n",
|
| 438 |
-
" <th>38</th>\n",
|
| 439 |
-
" <td>PYTVVYFPVRGRCAALRMLLADQGQSWKEEVVTVETWQEGSLKASC...</td>\n",
|
| 440 |
-
" <td>CC[C@H](C(=O)c1ccc(c(c1Cl)Cl)OCC(=O)O)C</td>\n",
|
| 441 |
-
" <td>1.5000</td>\n",
|
| 442 |
-
" </tr>\n",
|
| 443 |
-
" <tr>\n",
|
| 444 |
-
" <th>43</th>\n",
|
| 445 |
-
" <td>MPPYTVVYFPVRGRCAALRMLLADQGQSWKEEVVTVETWQEGSLKA...</td>\n",
|
| 446 |
-
" <td>OC(=O)c1cc(/N=N/c2ccc(cc2)S(=O)(=O)Nc2ccccn2)c...</td>\n",
|
| 447 |
-
" <td>24.0000</td>\n",
|
| 448 |
-
" </tr>\n",
|
| 449 |
-
" <tr>\n",
|
| 450 |
-
" <th>53</th>\n",
|
| 451 |
-
" <td>EKKSINECDLKGKKVLIRVDFNVPVKNGKITNDYRIRSALPTLKKV...</td>\n",
|
| 452 |
-
" <td>O[C@@H]1[C@@H](CO[P@](=O)(O[P@@](=O)(C(CCCC(P(...</td>\n",
|
| 453 |
-
" <td>6.0000</td>\n",
|
| 454 |
-
" </tr>\n",
|
| 455 |
-
" <tr>\n",
|
| 456 |
-
" <th>54</th>\n",
|
| 457 |
-
" <td>MPPYTVVYFPVRGRCAALRMLLADQGQSWKEEVVTVETWQEGSLKA...</td>\n",
|
| 458 |
-
" <td>CCCCCCSC[C@@H](C(=O)NCC(=O)O)NC(=O)CC[C@@H](C(...</td>\n",
|
| 459 |
-
" <td>10.0000</td>\n",
|
| 460 |
-
" </tr>\n",
|
| 461 |
-
" <tr>\n",
|
| 462 |
-
" <th>55</th>\n",
|
| 463 |
-
" <td>MNIFEMLRIDEGLRLKIYKDTEGYYTIGIGHLLTKSPSLNAAKSEL...</td>\n",
|
| 464 |
-
" <td>c1ccccc1</td>\n",
|
| 465 |
-
" <td>175.0000</td>\n",
|
| 466 |
-
" </tr>\n",
|
| 467 |
-
" <tr>\n",
|
| 468 |
-
" <th>...</th>\n",
|
| 469 |
-
" <td>...</td>\n",
|
| 470 |
-
" <td>...</td>\n",
|
| 471 |
-
" <td>...</td>\n",
|
| 472 |
-
" </tr>\n",
|
| 473 |
-
" <tr>\n",
|
| 474 |
-
" <th>105118</th>\n",
|
| 475 |
-
" <td>PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMNLPGRWKPKM...</td>\n",
|
| 476 |
-
" <td>O[C@@H]([C@H](Cc1ccccc1)NC(=O)[C@H](C(C)C)NC(=...</td>\n",
|
| 477 |
-
" <td>0.0045</td>\n",
|
| 478 |
-
" </tr>\n",
|
| 479 |
-
" <tr>\n",
|
| 480 |
-
" <th>105119</th>\n",
|
| 481 |
-
" <td>PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMNLPGRWKPKM...</td>\n",
|
| 482 |
-
" <td>O[C@@H]([C@H](Cc1ccccc1)NC(=O)[C@H](C(C)C)NC(=...</td>\n",
|
| 483 |
-
" <td>0.0045</td>\n",
|
| 484 |
-
" </tr>\n",
|
| 485 |
-
" <tr>\n",
|
| 486 |
-
" <th>105124</th>\n",
|
| 487 |
-
" <td>SKVVVPAQGKKITLQNGKLNVPENPIIPYIEGDGIGVDVTPAMLKV...</td>\n",
|
| 488 |
-
" <td>O[C@@H]1[C@@H](COP(=O)(O)O)O[C@H]([C@@H]1OP(=O...</td>\n",
|
| 489 |
-
" <td>125.0000</td>\n",
|
| 490 |
-
" </tr>\n",
|
| 491 |
-
" <tr>\n",
|
| 492 |
-
" <th>105133</th>\n",
|
| 493 |
-
" <td>ANIVGGIEYSINNASLCSVGFSVTRGATKGFVTAGHCGTVNATARI...</td>\n",
|
| 494 |
-
" <td>CC(C[C@@H](B(O)O)NC(=O)[C@@H]1CCCN1C(=O)[C@@H]...</td>\n",
|
| 495 |
-
" <td>2.0000</td>\n",
|
| 496 |
-
" </tr>\n",
|
| 497 |
-
" <tr>\n",
|
| 498 |
-
" <th>105138</th>\n",
|
| 499 |
-
" <td>KFPRVKNWELGSITYDTLCAQSQQDGPCTPRRCLGSLVLPRKLQTR...</td>\n",
|
| 500 |
-
" <td>CC[Se]C(=N)N</td>\n",
|
| 501 |
-
" <td>0.0390</td>\n",
|
| 502 |
-
" </tr>\n",
|
| 503 |
-
" </tbody>\n",
|
| 504 |
-
"</table>\n",
|
| 505 |
-
"<p>13645 rows × 3 columns</p>\n",
|
| 506 |
-
"</div>"
|
| 507 |
-
],
|
| 508 |
-
"text/plain": [
|
| 509 |
-
" seq \\\n",
|
| 510 |
-
"38 PYTVVYFPVRGRCAALRMLLADQGQSWKEEVVTVETWQEGSLKASC... \n",
|
| 511 |
-
"43 MPPYTVVYFPVRGRCAALRMLLADQGQSWKEEVVTVETWQEGSLKA... \n",
|
| 512 |
-
"53 EKKSINECDLKGKKVLIRVDFNVPVKNGKITNDYRIRSALPTLKKV... \n",
|
| 513 |
-
"54 MPPYTVVYFPVRGRCAALRMLLADQGQSWKEEVVTVETWQEGSLKA... \n",
|
| 514 |
-
"55 MNIFEMLRIDEGLRLKIYKDTEGYYTIGIGHLLTKSPSLNAAKSEL... \n",
|
| 515 |
-
"... ... \n",
|
| 516 |
-
"105118 PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMNLPGRWKPKM... \n",
|
| 517 |
-
"105119 PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMNLPGRWKPKM... \n",
|
| 518 |
-
"105124 SKVVVPAQGKKITLQNGKLNVPENPIIPYIEGDGIGVDVTPAMLKV... \n",
|
| 519 |
-
"105133 ANIVGGIEYSINNASLCSVGFSVTRGATKGFVTAGHCGTVNATARI... \n",
|
| 520 |
-
"105138 KFPRVKNWELGSITYDTLCAQSQQDGPCTPRRCLGSLVLPRKLQTR... \n",
|
| 521 |
-
"\n",
|
| 522 |
-
" smiles affinity_uM \n",
|
| 523 |
-
"38 CC[C@H](C(=O)c1ccc(c(c1Cl)Cl)OCC(=O)O)C 1.5000 \n",
|
| 524 |
-
"43 OC(=O)c1cc(/N=N/c2ccc(cc2)S(=O)(=O)Nc2ccccn2)c... 24.0000 \n",
|
| 525 |
-
"53 O[C@@H]1[C@@H](CO[P@](=O)(O[P@@](=O)(C(CCCC(P(... 6.0000 \n",
|
| 526 |
-
"54 CCCCCCSC[C@@H](C(=O)NCC(=O)O)NC(=O)CC[C@@H](C(... 10.0000 \n",
|
| 527 |
-
"55 c1ccccc1 175.0000 \n",
|
| 528 |
-
"... ... ... \n",
|
| 529 |
-
"105118 O[C@@H]([C@H](Cc1ccccc1)NC(=O)[C@H](C(C)C)NC(=... 0.0045 \n",
|
| 530 |
-
"105119 O[C@@H]([C@H](Cc1ccccc1)NC(=O)[C@H](C(C)C)NC(=... 0.0045 \n",
|
| 531 |
-
"105124 O[C@@H]1[C@@H](COP(=O)(O)O)O[C@H]([C@@H]1OP(=O... 125.0000 \n",
|
| 532 |
-
"105133 CC(C[C@@H](B(O)O)NC(=O)[C@@H]1CCCN1C(=O)[C@@H]... 2.0000 \n",
|
| 533 |
-
"105138 CC[Se]C(=N)N 0.0390 \n",
|
| 534 |
-
"\n",
|
| 535 |
-
"[13645 rows x 3 columns]"
|
| 536 |
-
]
|
| 537 |
-
},
|
| 538 |
-
"execution_count": 10,
|
| 539 |
-
"metadata": {},
|
| 540 |
-
"output_type": "execute_result"
|
| 541 |
-
}
|
| 542 |
-
],
|
| 543 |
-
"source": [
|
| 544 |
-
"df_biolip"
|
| 545 |
-
]
|
| 546 |
-
},
|
| 547 |
-
{
|
| 548 |
-
"cell_type": "code",
|
| 549 |
-
"execution_count": 11,
|
| 550 |
-
"id": "195f92db-fe06-4d03-8500-8d6c310a3347",
|
| 551 |
-
"metadata": {},
|
| 552 |
-
"outputs": [],
|
| 553 |
-
"source": [
|
| 554 |
-
"df_all = pd.concat([df_pdbbind,df_bindingdb,df_moad,df_biolip]).reset_index()"
|
| 555 |
-
]
|
| 556 |
-
},
|
| 557 |
-
{
|
| 558 |
-
"cell_type": "code",
|
| 559 |
-
"execution_count": 12,
|
| 560 |
-
"id": "d25c1e24-6566-4944-a0b4-944b3c8dbc6f",
|
| 561 |
-
"metadata": {},
|
| 562 |
-
"outputs": [
|
| 563 |
-
{
|
| 564 |
-
"data": {
|
| 565 |
-
"text/plain": [
|
| 566 |
-
"2283641"
|
| 567 |
-
]
|
| 568 |
-
},
|
| 569 |
-
"execution_count": 12,
|
| 570 |
-
"metadata": {},
|
| 571 |
-
"output_type": "execute_result"
|
| 572 |
-
}
|
| 573 |
-
],
|
| 574 |
-
"source": [
|
| 575 |
-
"len(df_all)"
|
| 576 |
-
]
|
| 577 |
-
},
|
| 578 |
-
{
|
| 579 |
-
"cell_type": "code",
|
| 580 |
-
"execution_count": 13,
|
| 581 |
-
"id": "c8287da2-cfdf-4d89-b175-f4c6b38ff8ac",
|
| 582 |
-
"metadata": {},
|
| 583 |
-
"outputs": [
|
| 584 |
-
{
|
| 585 |
-
"name": "stdout",
|
| 586 |
-
"output_type": "stream",
|
| 587 |
-
"text": [
|
| 588 |
-
"INFO: Pandarallel will run on 32 workers.\n",
|
| 589 |
-
"INFO: Pandarallel will use Memory file system to transfer data between the main process and workers.\n"
|
| 590 |
-
]
|
| 591 |
-
}
|
| 592 |
-
],
|
| 593 |
-
"source": [
|
| 594 |
-
"from pandarallel import pandarallel\n",
|
| 595 |
-
"pandarallel.initialize()"
|
| 596 |
-
]
|
| 597 |
-
},
|
| 598 |
-
{
|
| 599 |
-
"cell_type": "code",
|
| 600 |
-
"execution_count": null,
|
| 601 |
-
"id": "de5ffc4a-afb7-4a26-8d57-509c2278d750",
|
| 602 |
-
"metadata": {},
|
| 603 |
-
"outputs": [],
|
| 604 |
-
"source": [
|
| 605 |
-
"df_all['maccs'] = df_all['smiles'].parallel_apply(get_maccs)"
|
| 606 |
-
]
|
| 607 |
-
},
|
| 608 |
-
{
|
| 609 |
-
"cell_type": "code",
|
| 610 |
-
"execution_count": 16,
|
| 611 |
-
"id": "59a6706d-dab9-4ee0-8ef6-33537a3622a4",
|
| 612 |
-
"metadata": {},
|
| 613 |
-
"outputs": [],
|
| 614 |
-
"source": [
|
| 615 |
-
"df_all.to_parquet('data/all_maccs.parquet')"
|
| 616 |
-
]
|
| 617 |
-
},
|
| 618 |
-
{
|
| 619 |
-
"cell_type": "code",
|
| 620 |
-
"execution_count": 17,
|
| 621 |
-
"id": "4ccf2ee5-d369-4c0e-bb91-792765d661bf",
|
| 622 |
-
"metadata": {},
|
| 623 |
-
"outputs": [],
|
| 624 |
-
"source": [
|
| 625 |
-
"import numpy as np"
|
| 626 |
-
]
|
| 627 |
-
},
|
| 628 |
-
{
|
| 629 |
-
"cell_type": "code",
|
| 630 |
-
"execution_count": 18,
|
| 631 |
-
"id": "399f4ace-6dc3-441f-972a-f7b3a103e239",
|
| 632 |
-
"metadata": {},
|
| 633 |
-
"outputs": [
|
| 634 |
-
{
|
| 635 |
-
"data": {
|
| 636 |
-
"text/plain": [
|
| 637 |
-
"2283641"
|
| 638 |
-
]
|
| 639 |
-
},
|
| 640 |
-
"execution_count": 18,
|
| 641 |
-
"metadata": {},
|
| 642 |
-
"output_type": "execute_result"
|
| 643 |
-
}
|
| 644 |
-
],
|
| 645 |
-
"source": [
|
| 646 |
-
"len(df_all)"
|
| 647 |
-
]
|
| 648 |
-
},
|
| 649 |
-
{
|
| 650 |
-
"cell_type": "code",
|
| 651 |
-
"execution_count": 19,
|
| 652 |
-
"id": "8a4bbb18-e62f-4774-ac6b-8a1be68204c1",
|
| 653 |
-
"metadata": {},
|
| 654 |
-
"outputs": [],
|
| 655 |
-
"source": [
|
| 656 |
-
"df_all = pd.read_parquet('data/all_maccs.parquet')\n",
|
| 657 |
-
"df_all = df_all.dropna().reset_index(drop=True)"
|
| 658 |
-
]
|
| 659 |
-
},
|
| 660 |
-
{
|
| 661 |
-
"cell_type": "code",
|
| 662 |
-
"execution_count": 25,
|
| 663 |
-
"id": "d210fe56-a7eb-4adc-a77a-14c0c6d0034e",
|
| 664 |
-
"metadata": {},
|
| 665 |
-
"outputs": [
|
| 666 |
-
{
|
| 667 |
-
"data": {
|
| 668 |
-
"text/plain": [
|
| 669 |
-
"2277323"
|
| 670 |
-
]
|
| 671 |
-
},
|
| 672 |
-
"execution_count": 25,
|
| 673 |
-
"metadata": {},
|
| 674 |
-
"output_type": "execute_result"
|
| 675 |
-
}
|
| 676 |
-
],
|
| 677 |
-
"source": [
|
| 678 |
-
"len(df_all)"
|
| 679 |
-
]
|
| 680 |
-
},
|
| 681 |
-
{
|
| 682 |
-
"cell_type": "code",
|
| 683 |
-
"execution_count": 26,
|
| 684 |
-
"id": "d12b365d-98bd-4b61-b836-1a08d2e55418",
|
| 685 |
-
"metadata": {},
|
| 686 |
-
"outputs": [],
|
| 687 |
-
"source": [
|
| 688 |
-
"maccs = df_all['maccs'].to_numpy()\n",
|
| 689 |
-
"#df_reindex[df_reindex.duplicated(keep='first')].reset_index()"
|
| 690 |
-
]
|
| 691 |
-
},
|
| 692 |
-
{
|
| 693 |
-
"cell_type": "code",
|
| 694 |
-
"execution_count": 27,
|
| 695 |
-
"id": "80c15210-1af3-436e-970b-f81fc596fb41",
|
| 696 |
-
"metadata": {},
|
| 697 |
-
"outputs": [],
|
| 698 |
-
"source": [
|
| 699 |
-
"df_maccs = pd.DataFrame(np.vstack(maccs))"
|
| 700 |
-
]
|
| 701 |
-
},
|
| 702 |
-
{
|
| 703 |
-
"cell_type": "code",
|
| 704 |
-
"execution_count": 28,
|
| 705 |
-
"id": "30c314b8-8fe7-48ae-a2b8-149de1471b0c",
|
| 706 |
-
"metadata": {},
|
| 707 |
-
"outputs": [
|
| 708 |
-
{
|
| 709 |
-
"data": {
|
| 710 |
-
"text/plain": [
|
| 711 |
-
"0 int64\n",
|
| 712 |
-
"1 int64\n",
|
| 713 |
-
"2 int64\n",
|
| 714 |
-
"3 int64\n",
|
| 715 |
-
"4 int64\n",
|
| 716 |
-
"5 int64\n",
|
| 717 |
-
"dtype: object"
|
| 718 |
-
]
|
| 719 |
-
},
|
| 720 |
-
"execution_count": 28,
|
| 721 |
-
"metadata": {},
|
| 722 |
-
"output_type": "execute_result"
|
| 723 |
-
}
|
| 724 |
-
],
|
| 725 |
-
"source": [
|
| 726 |
-
"df_maccs.dtypes"
|
| 727 |
-
]
|
| 728 |
-
},
|
| 729 |
-
{
|
| 730 |
-
"cell_type": "code",
|
| 731 |
-
"execution_count": 29,
|
| 732 |
-
"id": "70a0a820-4d0c-4472-af96-9c301c0ab204",
|
| 733 |
-
"metadata": {},
|
| 734 |
-
"outputs": [],
|
| 735 |
-
"source": [
|
| 736 |
-
"df_expand = pd.concat([df_all[['seq','smiles','affinity_uM']],df_maccs],axis=1)"
|
| 737 |
-
]
|
| 738 |
-
},
|
| 739 |
-
{
|
| 740 |
-
"cell_type": "code",
|
| 741 |
-
"execution_count": 30,
|
| 742 |
-
"id": "13d092fa-5625-40d0-b7ec-e3405ea20279",
|
| 743 |
-
"metadata": {},
|
| 744 |
-
"outputs": [
|
| 745 |
-
{
|
| 746 |
-
"data": {
|
| 747 |
-
"text/html": [
|
| 748 |
-
"<div>\n",
|
| 749 |
-
"<style scoped>\n",
|
| 750 |
-
" .dataframe tbody tr th:only-of-type {\n",
|
| 751 |
-
" vertical-align: middle;\n",
|
| 752 |
-
" }\n",
|
| 753 |
-
"\n",
|
| 754 |
-
" .dataframe tbody tr th {\n",
|
| 755 |
-
" vertical-align: top;\n",
|
| 756 |
-
" }\n",
|
| 757 |
-
"\n",
|
| 758 |
-
" .dataframe thead th {\n",
|
| 759 |
-
" text-align: right;\n",
|
| 760 |
-
" }\n",
|
| 761 |
-
"</style>\n",
|
| 762 |
-
"<table border=\"1\" class=\"dataframe\">\n",
|
| 763 |
-
" <thead>\n",
|
| 764 |
-
" <tr style=\"text-align: right;\">\n",
|
| 765 |
-
" <th></th>\n",
|
| 766 |
-
" <th>seq</th>\n",
|
| 767 |
-
" <th>smiles</th>\n",
|
| 768 |
-
" <th>affinity_uM</th>\n",
|
| 769 |
-
" <th>0</th>\n",
|
| 770 |
-
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|
| 771 |
-
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|
| 772 |
-
" <th>3</th>\n",
|
| 773 |
-
" <th>4</th>\n",
|
| 774 |
-
" <th>5</th>\n",
|
| 775 |
-
" </tr>\n",
|
| 776 |
-
" </thead>\n",
|
| 777 |
-
" <tbody>\n",
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| 778 |
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" <tr>\n",
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| 779 |
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| 787 |
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|
| 788 |
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" <td>136</td>\n",
|
| 789 |
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" </tr>\n",
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| 790 |
-
" <tr>\n",
|
| 791 |
-
" <th>1</th>\n",
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| 792 |
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| 793 |
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| 797 |
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| 798 |
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| 799 |
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|
| 800 |
-
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|
| 801 |
-
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| 802 |
-
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| 804 |
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|
| 807 |
-
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|
| 808 |
-
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|
| 809 |
-
" <td>2123376961</td>\n",
|
| 810 |
-
" <td>3477340882</td>\n",
|
| 811 |
-
" <td>2951175957</td>\n",
|
| 812 |
-
" <td>252</td>\n",
|
| 813 |
-
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|
| 814 |
-
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|
| 815 |
-
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| 816 |
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| 818 |
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|
| 819 |
-
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|
| 820 |
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| 821 |
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| 822 |
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| 823 |
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|
| 824 |
-
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|
| 825 |
-
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| 826 |
-
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|
| 827 |
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| 831 |
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| 832 |
-
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|
| 833 |
-
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| 834 |
-
" <td>4081492984</td>\n",
|
| 835 |
-
" <td>4026260436</td>\n",
|
| 836 |
-
" <td>252</td>\n",
|
| 837 |
-
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| 846 |
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| 847 |
-
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|
| 848 |
-
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| 849 |
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|
| 850 |
-
" <tr>\n",
|
| 851 |
-
" <th>2277318</th>\n",
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| 854 |
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| 855 |
-
" <td>65536</td>\n",
|
| 856 |
-
" <td>393216</td>\n",
|
| 857 |
-
" <td>964698368</td>\n",
|
| 858 |
-
" <td>369403648</td>\n",
|
| 859 |
-
" <td>4284858000</td>\n",
|
| 860 |
-
" <td>252</td>\n",
|
| 861 |
-
" </tr>\n",
|
| 862 |
-
" <tr>\n",
|
| 863 |
-
" <th>2277319</th>\n",
|
| 864 |
-
" <td>PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMNLPGRWKPKM...</td>\n",
|
| 865 |
-
" <td>O[C@@H]([C@H](Cc1ccccc1)NC(=O)[C@H](C(C)C)NC(=...</td>\n",
|
| 866 |
-
" <td>0.0045</td>\n",
|
| 867 |
-
" <td>65536</td>\n",
|
| 868 |
-
" <td>393216</td>\n",
|
| 869 |
-
" <td>964698368</td>\n",
|
| 870 |
-
" <td>369403648</td>\n",
|
| 871 |
-
" <td>4284858000</td>\n",
|
| 872 |
-
" <td>252</td>\n",
|
| 873 |
-
" </tr>\n",
|
| 874 |
-
" <tr>\n",
|
| 875 |
-
" <th>2277320</th>\n",
|
| 876 |
-
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|
| 877 |
-
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|
| 878 |
-
" <td>125.0000</td>\n",
|
| 879 |
-
" <td>67108864</td>\n",
|
| 880 |
-
" <td>1115688962</td>\n",
|
| 881 |
-
" <td>1771869508</td>\n",
|
| 882 |
-
" <td>4018431718</td>\n",
|
| 883 |
-
" <td>3744193341</td>\n",
|
| 884 |
-
" <td>124</td>\n",
|
| 885 |
-
" </tr>\n",
|
| 886 |
-
" <tr>\n",
|
| 887 |
-
" <th>2277321</th>\n",
|
| 888 |
-
" <td>ANIVGGIEYSINNASLCSVGFSVTRGATKGFVTAGHCGTVNATARI...</td>\n",
|
| 889 |
-
" <td>CC(C[C@@H](B(O)O)NC(=O)[C@@H]1CCCN1C(=O)[C@@H]...</td>\n",
|
| 890 |
-
" <td>2.0000</td>\n",
|
| 891 |
-
" <td>2097152</td>\n",
|
| 892 |
-
" <td>137216</td>\n",
|
| 893 |
-
" <td>958148868</td>\n",
|
| 894 |
-
" <td>1746307978</td>\n",
|
| 895 |
-
" <td>2067783280</td>\n",
|
| 896 |
-
" <td>204</td>\n",
|
| 897 |
-
" </tr>\n",
|
| 898 |
-
" <tr>\n",
|
| 899 |
-
" <th>2277322</th>\n",
|
| 900 |
-
" <td>KFPRVKNWELGSITYDTLCAQSQQDGPCTPRRCLGSLVLPRKLQTR...</td>\n",
|
| 901 |
-
" <td>CC[Se]C(=N)N</td>\n",
|
| 902 |
-
" <td>0.0390</td>\n",
|
| 903 |
-
" <td>16</td>\n",
|
| 904 |
-
" <td>6144</td>\n",
|
| 905 |
-
" <td>537396736</td>\n",
|
| 906 |
-
" <td>2170880</td>\n",
|
| 907 |
-
" <td>1510015504</td>\n",
|
| 908 |
-
" <td>192</td>\n",
|
| 909 |
-
" </tr>\n",
|
| 910 |
-
" </tbody>\n",
|
| 911 |
-
"</table>\n",
|
| 912 |
-
"<p>2277323 rows × 9 columns</p>\n",
|
| 913 |
-
"</div>"
|
| 914 |
-
],
|
| 915 |
-
"text/plain": [
|
| 916 |
-
" seq \\\n",
|
| 917 |
-
"0 MTVPDRSEIAGKWYVVALASNTEFFLREKDKMKMAMARISFLGEDE... \n",
|
| 918 |
-
"1 APQTITELCSEYRNTQIYTINDKILSYTESMAGKREMVIITFKSGE... \n",
|
| 919 |
-
"2 VETFAFQAEIAQLMSLIINTFYSNKEIFLRELISNSSDALDKIRYE... \n",
|
| 920 |
-
"3 AAPFDKSKNVAQSIDQLIGQTPALYLNKLNNTKAKVVLKMECENPM... \n",
|
| 921 |
-
"4 YITFRSFTAVLIAFFLTLVLSPSFINRLRKIQRKKYTPTMGGIVIL... \n",
|
| 922 |
-
"... ... \n",
|
| 923 |
-
"2277318 PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMNLPGRWKPKM... \n",
|
| 924 |
-
"2277319 PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMNLPGRWKPKM... \n",
|
| 925 |
-
"2277320 SKVVVPAQGKKITLQNGKLNVPENPIIPYIEGDGIGVDVTPAMLKV... \n",
|
| 926 |
-
"2277321 ANIVGGIEYSINNASLCSVGFSVTRGATKGFVTAGHCGTVNATARI... \n",
|
| 927 |
-
"2277322 KFPRVKNWELGSITYDTLCAQSQQDGPCTPRRCLGSLVLPRKLQTR... \n",
|
| 928 |
-
"\n",
|
| 929 |
-
" smiles affinity_uM \\\n",
|
| 930 |
-
"0 CCCCCCCCCCCCCCCCCCCC(=O)O 0.0260 \n",
|
| 931 |
-
"1 OC[C@H]1O[C@H](Oc2cccc(c2)N(=O)=O)[C@@H]([C@H]... 500.0000 \n",
|
| 932 |
-
"2 COc1ccc(cc1)c1c(onc1c1cc(C(C)C)c(cc1O)O)NC(=O)... 0.0230 \n",
|
| 933 |
-
"3 OC[C@@H](C(=O)N[C@@H]([C@H](CC)C)C(=O)O)NC(=O)... 6.4300 \n",
|
| 934 |
-
"4 CO[C@@H]1[C@H](O[C@H]([C@@H]1O)n1ccc(=O)[nH]c1... 0.1850 \n",
|
| 935 |
-
"... ... ... \n",
|
| 936 |
-
"2277318 O[C@@H]([C@H](Cc1ccccc1)NC(=O)[C@H](C(C)C)NC(=... 0.0045 \n",
|
| 937 |
-
"2277319 O[C@@H]([C@H](Cc1ccccc1)NC(=O)[C@H](C(C)C)NC(=... 0.0045 \n",
|
| 938 |
-
"2277320 O[C@@H]1[C@@H](COP(=O)(O)O)O[C@H]([C@@H]1OP(=O... 125.0000 \n",
|
| 939 |
-
"2277321 CC(C[C@@H](B(O)O)NC(=O)[C@@H]1CCCN1C(=O)[C@@H]... 2.0000 \n",
|
| 940 |
-
"2277322 CC[Se]C(=N)N 0.0390 \n",
|
| 941 |
-
"\n",
|
| 942 |
-
" 0 1 2 3 4 5 \n",
|
| 943 |
-
"0 0 0 805306368 272271360 890245320 136 \n",
|
| 944 |
-
"1 2147483648 3242590208 1914732547 994116706 3748288829 124 \n",
|
| 945 |
-
"2 131072 1109655552 2123376961 3477340882 2951175957 252 \n",
|
| 946 |
-
"3 0 6685696 2033191680 1345701844 2133187096 220 \n",
|
| 947 |
-
"4 1048576 1107427332 2109513024 4081492984 4026260436 252 \n",
|
| 948 |
-
"... ... ... ... ... ... ... \n",
|
| 949 |
-
"2277318 65536 393216 964698368 369403648 4284858000 252 \n",
|
| 950 |
-
"2277319 65536 393216 964698368 369403648 4284858000 252 \n",
|
| 951 |
-
"2277320 67108864 1115688962 1771869508 4018431718 3744193341 124 \n",
|
| 952 |
-
"2277321 2097152 137216 958148868 1746307978 2067783280 204 \n",
|
| 953 |
-
"2277322 16 6144 537396736 2170880 1510015504 192 \n",
|
| 954 |
-
"\n",
|
| 955 |
-
"[2277323 rows x 9 columns]"
|
| 956 |
-
]
|
| 957 |
-
},
|
| 958 |
-
"execution_count": 30,
|
| 959 |
-
"metadata": {},
|
| 960 |
-
"output_type": "execute_result"
|
| 961 |
-
}
|
| 962 |
-
],
|
| 963 |
-
"source": [
|
| 964 |
-
"df_expand"
|
| 965 |
-
]
|
| 966 |
-
},
|
| 967 |
-
{
|
| 968 |
-
"cell_type": "code",
|
| 969 |
-
"execution_count": 31,
|
| 970 |
-
"id": "30f7fff7-3cfe-41c8-97c9-666f3e256222",
|
| 971 |
-
"metadata": {},
|
| 972 |
-
"outputs": [
|
| 973 |
-
{
|
| 974 |
-
"data": {
|
| 975 |
-
"text/plain": [
|
| 976 |
-
"Index(['seq', 'smiles', 'affinity_uM', 0, 1, 2, 3, 4, 5], dtype='object')"
|
| 977 |
-
]
|
| 978 |
-
},
|
| 979 |
-
"execution_count": 31,
|
| 980 |
-
"metadata": {},
|
| 981 |
-
"output_type": "execute_result"
|
| 982 |
-
}
|
| 983 |
-
],
|
| 984 |
-
"source": [
|
| 985 |
-
"df_expand.columns"
|
| 986 |
-
]
|
| 987 |
-
},
|
| 988 |
-
{
|
| 989 |
-
"cell_type": "code",
|
| 990 |
-
"execution_count": 32,
|
| 991 |
-
"id": "16d2b26e-984f-4c71-af19-a3e711ed9ca2",
|
| 992 |
-
"metadata": {},
|
| 993 |
-
"outputs": [],
|
| 994 |
-
"source": [
|
| 995 |
-
"df_reindex = df_expand.set_index([0,1,2,3,4,5,'seq'])"
|
| 996 |
-
]
|
| 997 |
-
},
|
| 998 |
-
{
|
| 999 |
-
"cell_type": "code",
|
| 1000 |
-
"execution_count": 33,
|
| 1001 |
-
"id": "27fa2150-8152-444b-ba5b-24bea39fc098",
|
| 1002 |
-
"metadata": {},
|
| 1003 |
-
"outputs": [
|
| 1004 |
-
{
|
| 1005 |
-
"data": {
|
| 1006 |
-
"text/plain": [
|
| 1007 |
-
"Index(['smiles', 'affinity_uM'], dtype='object')"
|
| 1008 |
-
]
|
| 1009 |
-
},
|
| 1010 |
-
"execution_count": 33,
|
| 1011 |
-
"metadata": {},
|
| 1012 |
-
"output_type": "execute_result"
|
| 1013 |
-
}
|
| 1014 |
-
],
|
| 1015 |
-
"source": [
|
| 1016 |
-
"df_reindex.columns"
|
| 1017 |
-
]
|
| 1018 |
-
},
|
| 1019 |
-
{
|
| 1020 |
-
"cell_type": "code",
|
| 1021 |
-
"execution_count": 34,
|
| 1022 |
-
"id": "89edacbc-52f3-4a76-90b0-95273f5e53b3",
|
| 1023 |
-
"metadata": {},
|
| 1024 |
-
"outputs": [],
|
| 1025 |
-
"source": [
|
| 1026 |
-
"df_nr = df_reindex[~df_reindex.duplicated(keep='first')].reset_index()\n",
|
| 1027 |
-
"df_nr = df_nr.drop(columns=[0,1,2,3,4,5])"
|
| 1028 |
-
]
|
| 1029 |
-
},
|
| 1030 |
-
{
|
| 1031 |
-
"cell_type": "code",
|
| 1032 |
-
"execution_count": 36,
|
| 1033 |
-
"id": "6a704c5e-68a6-418f-bcad-8688a13ca1d6",
|
| 1034 |
-
"metadata": {},
|
| 1035 |
-
"outputs": [],
|
| 1036 |
-
"source": [
|
| 1037 |
-
"# final sanity checks"
|
| 1038 |
-
]
|
| 1039 |
-
},
|
| 1040 |
-
{
|
| 1041 |
-
"cell_type": "code",
|
| 1042 |
-
"execution_count": 37,
|
| 1043 |
-
"id": "0cad3882-975d-4693-aad1-63ec26646bd0",
|
| 1044 |
-
"metadata": {},
|
| 1045 |
-
"outputs": [
|
| 1046 |
-
{
|
| 1047 |
-
"name": "stderr",
|
| 1048 |
-
"output_type": "stream",
|
| 1049 |
-
"text": [
|
| 1050 |
-
"/ccs/proj/stf006/glaser/conda-envs/dask/lib/python3.9/site-packages/pandas/core/arraylike.py:358: RuntimeWarning: divide by zero encountered in log\n",
|
| 1051 |
-
" result = getattr(ufunc, method)(*inputs, **kwargs)\n"
|
| 1052 |
-
]
|
| 1053 |
-
}
|
| 1054 |
-
],
|
| 1055 |
-
"source": [
|
| 1056 |
-
"df_nr['neg_log10_affinity_M'] = 6-np.log(df_nr['affinity_uM'])/np.log(10)"
|
| 1057 |
-
]
|
| 1058 |
-
},
|
| 1059 |
-
{
|
| 1060 |
-
"cell_type": "code",
|
| 1061 |
-
"execution_count": 38,
|
| 1062 |
-
"id": "c200e29a-3f14-41f4-b620-ccce0eb0d5ce",
|
| 1063 |
-
"metadata": {},
|
| 1064 |
-
"outputs": [
|
| 1065 |
-
{
|
| 1066 |
-
"data": {
|
| 1067 |
-
"text/html": [
|
| 1068 |
-
"<div>\n",
|
| 1069 |
-
"<style scoped>\n",
|
| 1070 |
-
" .dataframe tbody tr th:only-of-type {\n",
|
| 1071 |
-
" vertical-align: middle;\n",
|
| 1072 |
-
" }\n",
|
| 1073 |
-
"\n",
|
| 1074 |
-
" .dataframe tbody tr th {\n",
|
| 1075 |
-
" vertical-align: top;\n",
|
| 1076 |
-
" }\n",
|
| 1077 |
-
"\n",
|
| 1078 |
-
" .dataframe thead th {\n",
|
| 1079 |
-
" text-align: right;\n",
|
| 1080 |
-
" }\n",
|
| 1081 |
-
"</style>\n",
|
| 1082 |
-
"<table border=\"1\" class=\"dataframe\">\n",
|
| 1083 |
-
" <thead>\n",
|
| 1084 |
-
" <tr style=\"text-align: right;\">\n",
|
| 1085 |
-
" <th></th>\n",
|
| 1086 |
-
" <th>seq</th>\n",
|
| 1087 |
-
" <th>smiles</th>\n",
|
| 1088 |
-
" <th>affinity_uM</th>\n",
|
| 1089 |
-
" <th>neg_log10_affinity_M</th>\n",
|
| 1090 |
-
" </tr>\n",
|
| 1091 |
-
" </thead>\n",
|
| 1092 |
-
" <tbody>\n",
|
| 1093 |
-
" <tr>\n",
|
| 1094 |
-
" <th>0</th>\n",
|
| 1095 |
-
" <td>MTVPDRSEIAGKWYVVALASNTEFFLREKDKMKMAMARISFLGEDE...</td>\n",
|
| 1096 |
-
" <td>CCCCCCCCCCCCCCCCCCCC(=O)O</td>\n",
|
| 1097 |
-
" <td>0.0260</td>\n",
|
| 1098 |
-
" <td>7.585027</td>\n",
|
| 1099 |
-
" </tr>\n",
|
| 1100 |
-
" <tr>\n",
|
| 1101 |
-
" <th>1</th>\n",
|
| 1102 |
-
" <td>APQTITELCSEYRNTQIYTINDKILSYTESMAGKREMVIITFKSGE...</td>\n",
|
| 1103 |
-
" <td>OC[C@H]1O[C@H](Oc2cccc(c2)N(=O)=O)[C@@H]([C@H]...</td>\n",
|
| 1104 |
-
" <td>500.0000</td>\n",
|
| 1105 |
-
" <td>3.301030</td>\n",
|
| 1106 |
-
" </tr>\n",
|
| 1107 |
-
" <tr>\n",
|
| 1108 |
-
" <th>2</th>\n",
|
| 1109 |
-
" <td>VETFAFQAEIAQLMSLIINTFYSNKEIFLRELISNSSDALDKIRYE...</td>\n",
|
| 1110 |
-
" <td>COc1ccc(cc1)c1c(onc1c1cc(C(C)C)c(cc1O)O)NC(=O)...</td>\n",
|
| 1111 |
-
" <td>0.0230</td>\n",
|
| 1112 |
-
" <td>7.638272</td>\n",
|
| 1113 |
-
" </tr>\n",
|
| 1114 |
-
" <tr>\n",
|
| 1115 |
-
" <th>3</th>\n",
|
| 1116 |
-
" <td>AAPFDKSKNVAQSIDQLIGQTPALYLNKLNNTKAKVVLKMECENPM...</td>\n",
|
| 1117 |
-
" <td>OC[C@@H](C(=O)N[C@@H]([C@H](CC)C)C(=O)O)NC(=O)...</td>\n",
|
| 1118 |
-
" <td>6.4300</td>\n",
|
| 1119 |
-
" <td>5.191789</td>\n",
|
| 1120 |
-
" </tr>\n",
|
| 1121 |
-
" <tr>\n",
|
| 1122 |
-
" <th>4</th>\n",
|
| 1123 |
-
" <td>YITFRSFTAVLIAFFLTLVLSPSFINRLRKIQRKKYTPTMGGIVIL...</td>\n",
|
| 1124 |
-
" <td>CO[C@@H]1[C@H](O[C@H]([C@@H]1O)n1ccc(=O)[nH]c1...</td>\n",
|
| 1125 |
-
" <td>0.1850</td>\n",
|
| 1126 |
-
" <td>6.732828</td>\n",
|
| 1127 |
-
" </tr>\n",
|
| 1128 |
-
" <tr>\n",
|
| 1129 |
-
" <th>...</th>\n",
|
| 1130 |
-
" <td>...</td>\n",
|
| 1131 |
-
" <td>...</td>\n",
|
| 1132 |
-
" <td>...</td>\n",
|
| 1133 |
-
" <td>...</td>\n",
|
| 1134 |
-
" </tr>\n",
|
| 1135 |
-
" <tr>\n",
|
| 1136 |
-
" <th>1838495</th>\n",
|
| 1137 |
-
" <td>IVEGSDAEIGMSPWQVMLFRKSPQELLCGASLISDRWVLTAAHCLL...</td>\n",
|
| 1138 |
-
" <td>O=C[C@@H](NC(=O)[C@H](Cc1ccc(cc1)OS(O)(O)O)NC(...</td>\n",
|
| 1139 |
-
" <td>8.0000</td>\n",
|
| 1140 |
-
" <td>5.096910</td>\n",
|
| 1141 |
-
" </tr>\n",
|
| 1142 |
-
" <tr>\n",
|
| 1143 |
-
" <th>1838496</th>\n",
|
| 1144 |
-
" <td>IVEGSDAEIGMSPWQVMLFRKSPQELLCGASLISDRWVLTAAHCLL...</td>\n",
|
| 1145 |
-
" <td>CC(C[C@@H](C(=O)N1C=CC[C@H]1C(=O)N)NC(=O)[C@@H...</td>\n",
|
| 1146 |
-
" <td>8.0000</td>\n",
|
| 1147 |
-
" <td>5.096910</td>\n",
|
| 1148 |
-
" </tr>\n",
|
| 1149 |
-
" <tr>\n",
|
| 1150 |
-
" <th>1838497</th>\n",
|
| 1151 |
-
" <td>PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMNLPGRWKPKM...</td>\n",
|
| 1152 |
-
" <td>O[C@@H]([C@H](Cc1ccccc1)NC(=O)[C@H](C(C)C)NC(=...</td>\n",
|
| 1153 |
-
" <td>0.0045</td>\n",
|
| 1154 |
-
" <td>8.346787</td>\n",
|
| 1155 |
-
" </tr>\n",
|
| 1156 |
-
" <tr>\n",
|
| 1157 |
-
" <th>1838498</th>\n",
|
| 1158 |
-
" <td>ANIVGGIEYSINNASLCSVGFSVTRGATKGFVTAGHCGTVNATARI...</td>\n",
|
| 1159 |
-
" <td>CC(C[C@@H](B(O)O)NC(=O)[C@@H]1CCCN1C(=O)[C@@H]...</td>\n",
|
| 1160 |
-
" <td>2.0000</td>\n",
|
| 1161 |
-
" <td>5.698970</td>\n",
|
| 1162 |
-
" </tr>\n",
|
| 1163 |
-
" <tr>\n",
|
| 1164 |
-
" <th>1838499</th>\n",
|
| 1165 |
-
" <td>KFPRVKNWELGSITYDTLCAQSQQDGPCTPRRCLGSLVLPRKLQTR...</td>\n",
|
| 1166 |
-
" <td>CC[Se]C(=N)N</td>\n",
|
| 1167 |
-
" <td>0.0390</td>\n",
|
| 1168 |
-
" <td>7.408935</td>\n",
|
| 1169 |
-
" </tr>\n",
|
| 1170 |
-
" </tbody>\n",
|
| 1171 |
-
"</table>\n",
|
| 1172 |
-
"<p>1838500 rows × 4 columns</p>\n",
|
| 1173 |
-
"</div>"
|
| 1174 |
-
],
|
| 1175 |
-
"text/plain": [
|
| 1176 |
-
" seq \\\n",
|
| 1177 |
-
"0 MTVPDRSEIAGKWYVVALASNTEFFLREKDKMKMAMARISFLGEDE... \n",
|
| 1178 |
-
"1 APQTITELCSEYRNTQIYTINDKILSYTESMAGKREMVIITFKSGE... \n",
|
| 1179 |
-
"2 VETFAFQAEIAQLMSLIINTFYSNKEIFLRELISNSSDALDKIRYE... \n",
|
| 1180 |
-
"3 AAPFDKSKNVAQSIDQLIGQTPALYLNKLNNTKAKVVLKMECENPM... \n",
|
| 1181 |
-
"4 YITFRSFTAVLIAFFLTLVLSPSFINRLRKIQRKKYTPTMGGIVIL... \n",
|
| 1182 |
-
"... ... \n",
|
| 1183 |
-
"1838495 IVEGSDAEIGMSPWQVMLFRKSPQELLCGASLISDRWVLTAAHCLL... \n",
|
| 1184 |
-
"1838496 IVEGSDAEIGMSPWQVMLFRKSPQELLCGASLISDRWVLTAAHCLL... \n",
|
| 1185 |
-
"1838497 PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMNLPGRWKPKM... \n",
|
| 1186 |
-
"1838498 ANIVGGIEYSINNASLCSVGFSVTRGATKGFVTAGHCGTVNATARI... \n",
|
| 1187 |
-
"1838499 KFPRVKNWELGSITYDTLCAQSQQDGPCTPRRCLGSLVLPRKLQTR... \n",
|
| 1188 |
-
"\n",
|
| 1189 |
-
" smiles affinity_uM \\\n",
|
| 1190 |
-
"0 CCCCCCCCCCCCCCCCCCCC(=O)O 0.0260 \n",
|
| 1191 |
-
"1 OC[C@H]1O[C@H](Oc2cccc(c2)N(=O)=O)[C@@H]([C@H]... 500.0000 \n",
|
| 1192 |
-
"2 COc1ccc(cc1)c1c(onc1c1cc(C(C)C)c(cc1O)O)NC(=O)... 0.0230 \n",
|
| 1193 |
-
"3 OC[C@@H](C(=O)N[C@@H]([C@H](CC)C)C(=O)O)NC(=O)... 6.4300 \n",
|
| 1194 |
-
"4 CO[C@@H]1[C@H](O[C@H]([C@@H]1O)n1ccc(=O)[nH]c1... 0.1850 \n",
|
| 1195 |
-
"... ... ... \n",
|
| 1196 |
-
"1838495 O=C[C@@H](NC(=O)[C@H](Cc1ccc(cc1)OS(O)(O)O)NC(... 8.0000 \n",
|
| 1197 |
-
"1838496 CC(C[C@@H](C(=O)N1C=CC[C@H]1C(=O)N)NC(=O)[C@@H... 8.0000 \n",
|
| 1198 |
-
"1838497 O[C@@H]([C@H](Cc1ccccc1)NC(=O)[C@H](C(C)C)NC(=... 0.0045 \n",
|
| 1199 |
-
"1838498 CC(C[C@@H](B(O)O)NC(=O)[C@@H]1CCCN1C(=O)[C@@H]... 2.0000 \n",
|
| 1200 |
-
"1838499 CC[Se]C(=N)N 0.0390 \n",
|
| 1201 |
-
"\n",
|
| 1202 |
-
" neg_log10_affinity_M \n",
|
| 1203 |
-
"0 7.585027 \n",
|
| 1204 |
-
"1 3.301030 \n",
|
| 1205 |
-
"2 7.638272 \n",
|
| 1206 |
-
"3 5.191789 \n",
|
| 1207 |
-
"4 6.732828 \n",
|
| 1208 |
-
"... ... \n",
|
| 1209 |
-
"1838495 5.096910 \n",
|
| 1210 |
-
"1838496 5.096910 \n",
|
| 1211 |
-
"1838497 8.346787 \n",
|
| 1212 |
-
"1838498 5.698970 \n",
|
| 1213 |
-
"1838499 7.408935 \n",
|
| 1214 |
-
"\n",
|
| 1215 |
-
"[1838500 rows x 4 columns]"
|
| 1216 |
-
]
|
| 1217 |
-
},
|
| 1218 |
-
"execution_count": 38,
|
| 1219 |
-
"metadata": {},
|
| 1220 |
-
"output_type": "execute_result"
|
| 1221 |
-
}
|
| 1222 |
-
],
|
| 1223 |
-
"source": [
|
| 1224 |
-
"df_nr"
|
| 1225 |
-
]
|
| 1226 |
-
},
|
| 1227 |
-
{
|
| 1228 |
-
"cell_type": "code",
|
| 1229 |
-
"execution_count": 52,
|
| 1230 |
-
"id": "7f4027a2-0a5f-47bf-8a34-0c6a73b9b112",
|
| 1231 |
-
"metadata": {},
|
| 1232 |
-
"outputs": [],
|
| 1233 |
-
"source": [
|
| 1234 |
-
"df = df_nr[np.isfinite(df_nr['neg_log10_affinity_M'])].copy()"
|
| 1235 |
-
]
|
| 1236 |
-
},
|
| 1237 |
-
{
|
| 1238 |
-
"cell_type": "code",
|
| 1239 |
-
"execution_count": 53,
|
| 1240 |
-
"id": "eb99774f-9bcc-454d-b5e5-a8470223d6ca",
|
| 1241 |
-
"metadata": {},
|
| 1242 |
-
"outputs": [],
|
| 1243 |
-
"source": [
|
| 1244 |
-
"from rdkit import Chem\n",
|
| 1245 |
-
"def make_canonical(smi):\n",
|
| 1246 |
-
" try:\n",
|
| 1247 |
-
" return Chem.MolToSmiles(Chem.MolFromSmiles(smi))\n",
|
| 1248 |
-
" except:\n",
|
| 1249 |
-
" return smi"
|
| 1250 |
-
]
|
| 1251 |
-
},
|
| 1252 |
-
{
|
| 1253 |
-
"cell_type": "code",
|
| 1254 |
-
"execution_count": 54,
|
| 1255 |
-
"id": "4d44bd8e-f2e1-44b4-aea7-40b4437baf44",
|
| 1256 |
-
"metadata": {},
|
| 1257 |
-
"outputs": [],
|
| 1258 |
-
"source": [
|
| 1259 |
-
"df['smiles_can'] = df['smiles'].parallel_apply(make_canonical)"
|
| 1260 |
-
]
|
| 1261 |
-
},
|
| 1262 |
-
{
|
| 1263 |
-
"cell_type": "code",
|
| 1264 |
-
"execution_count": 55,
|
| 1265 |
-
"id": "07ffdeb1-f4fa-4776-9fea-a18439e03d2e",
|
| 1266 |
-
"metadata": {},
|
| 1267 |
-
"outputs": [],
|
| 1268 |
-
"source": [
|
| 1269 |
-
"df = df[(df['neg_log10_affinity_M']>0) & (df['neg_log10_affinity_M']<15)].reset_index()"
|
| 1270 |
-
]
|
| 1271 |
-
},
|
| 1272 |
-
{
|
| 1273 |
-
"cell_type": "code",
|
| 1274 |
-
"execution_count": 56,
|
| 1275 |
-
"id": "8f949038-d07d-4d3a-a47e-b825cc9018ca",
|
| 1276 |
-
"metadata": {},
|
| 1277 |
-
"outputs": [],
|
| 1278 |
-
"source": [
|
| 1279 |
-
"from sklearn.preprocessing import StandardScaler"
|
| 1280 |
-
]
|
| 1281 |
-
},
|
| 1282 |
-
{
|
| 1283 |
-
"cell_type": "code",
|
| 1284 |
-
"execution_count": 57,
|
| 1285 |
-
"id": "0c027988-0b44-4010-ad61-7d70eead1654",
|
| 1286 |
-
"metadata": {},
|
| 1287 |
-
"outputs": [],
|
| 1288 |
-
"source": [
|
| 1289 |
-
"scaler = StandardScaler()"
|
| 1290 |
-
]
|
| 1291 |
-
},
|
| 1292 |
-
{
|
| 1293 |
-
"cell_type": "code",
|
| 1294 |
-
"execution_count": 58,
|
| 1295 |
-
"id": "6aeba020-b6ff-4633-902e-4df74463eb2f",
|
| 1296 |
-
"metadata": {},
|
| 1297 |
-
"outputs": [],
|
| 1298 |
-
"source": [
|
| 1299 |
-
"df['affinity'] = scaler.fit_transform(df['neg_log10_affinity_M'].values.reshape(-1,1))"
|
| 1300 |
-
]
|
| 1301 |
-
},
|
| 1302 |
-
{
|
| 1303 |
-
"cell_type": "code",
|
| 1304 |
-
"execution_count": 59,
|
| 1305 |
-
"id": "91196eee-5fd0-4aa4-927a-5c1a3f436ac8",
|
| 1306 |
-
"metadata": {},
|
| 1307 |
-
"outputs": [
|
| 1308 |
-
{
|
| 1309 |
-
"data": {
|
| 1310 |
-
"text/plain": [
|
| 1311 |
-
"(array([6.50604534]), array([2.43319576]))"
|
| 1312 |
-
]
|
| 1313 |
-
},
|
| 1314 |
-
"execution_count": 59,
|
| 1315 |
-
"metadata": {},
|
| 1316 |
-
"output_type": "execute_result"
|
| 1317 |
-
}
|
| 1318 |
-
],
|
| 1319 |
-
"source": [
|
| 1320 |
-
"scaler.mean_, scaler.var_"
|
| 1321 |
-
]
|
| 1322 |
-
},
|
| 1323 |
-
{
|
| 1324 |
-
"cell_type": "code",
|
| 1325 |
-
"execution_count": 60,
|
| 1326 |
-
"id": "56269dcb-e691-4759-949d-7bfdd02f5fd4",
|
| 1327 |
-
"metadata": {},
|
| 1328 |
-
"outputs": [],
|
| 1329 |
-
"source": [
|
| 1330 |
-
"df = df.drop(columns='index')"
|
| 1331 |
-
]
|
| 1332 |
-
},
|
| 1333 |
-
{
|
| 1334 |
-
"cell_type": "code",
|
| 1335 |
-
"execution_count": 7,
|
| 1336 |
-
"id": "c6c64066-4032-4247-a8b9-00388176cc7b",
|
| 1337 |
-
"metadata": {},
|
| 1338 |
-
"outputs": [],
|
| 1339 |
-
"source": [
|
| 1340 |
-
"df = df.astype({'affinity_uM': 'float32', 'neg_log10_affinity_M': 'float32', 'affinity': 'float32'})\n",
|
| 1341 |
-
"df.to_parquet('data/all.parquet')\n",
|
| 1342 |
-
"\n",
|
| 1343 |
-
"#df = pd.read_parquet('data/all.parquet')"
|
| 1344 |
-
]
|
| 1345 |
-
},
|
| 1346 |
-
{
|
| 1347 |
-
"cell_type": "code",
|
| 1348 |
-
"execution_count": 14,
|
| 1349 |
-
"id": "469cf0dd-7b87-4245-973c-2a445e1fcca9",
|
| 1350 |
-
"metadata": {},
|
| 1351 |
-
"outputs": [
|
| 1352 |
-
{
|
| 1353 |
-
"data": {
|
| 1354 |
-
"text/plain": [
|
| 1355 |
-
"Index(['seq', 'smiles', 'affinity_uM', 'neg_log10_affinity_M', 'smiles_can',\n",
|
| 1356 |
-
" 'affinity'],\n",
|
| 1357 |
-
" dtype='object')"
|
| 1358 |
-
]
|
| 1359 |
-
},
|
| 1360 |
-
"execution_count": 14,
|
| 1361 |
-
"metadata": {},
|
| 1362 |
-
"output_type": "execute_result"
|
| 1363 |
-
}
|
| 1364 |
-
],
|
| 1365 |
-
"source": [
|
| 1366 |
-
"df.columns"
|
| 1367 |
-
]
|
| 1368 |
-
},
|
| 1369 |
-
{
|
| 1370 |
-
"cell_type": "code",
|
| 1371 |
-
"execution_count": 63,
|
| 1372 |
-
"id": "d91c0d91-474c-4ab2-9a5e-3b7861f7a832",
|
| 1373 |
-
"metadata": {},
|
| 1374 |
-
"outputs": [
|
| 1375 |
-
{
|
| 1376 |
-
"data": {
|
| 1377 |
-
"image/png": 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\n",
|
| 1378 |
-
"text/plain": [
|
| 1379 |
-
"<Figure size 432x288 with 1 Axes>"
|
| 1380 |
-
]
|
| 1381 |
-
},
|
| 1382 |
-
"metadata": {
|
| 1383 |
-
"needs_background": "light"
|
| 1384 |
-
},
|
| 1385 |
-
"output_type": "display_data"
|
| 1386 |
-
}
|
| 1387 |
-
],
|
| 1388 |
-
"source": [
|
| 1389 |
-
"ax = df['neg_log10_affinity_M'].hist(bins=100,density=True)\n",
|
| 1390 |
-
"ax.set_xlabel('-$\\log_{10}$ affinity[M]',fontsize=16)\n",
|
| 1391 |
-
"ax.set_ylabel('probability',fontsize=16)\n",
|
| 1392 |
-
"ax.figure.savefig('affinity_neglog10_M.pdf')"
|
| 1393 |
-
]
|
| 1394 |
-
},
|
| 1395 |
-
{
|
| 1396 |
-
"cell_type": "code",
|
| 1397 |
-
"execution_count": 64,
|
| 1398 |
-
"id": "0e895ef5-1812-46c7-a4c2-dd6619b49157",
|
| 1399 |
-
"metadata": {},
|
| 1400 |
-
"outputs": [
|
| 1401 |
-
{
|
| 1402 |
-
"data": {
|
| 1403 |
-
"text/plain": [
|
| 1404 |
-
"1836729"
|
| 1405 |
-
]
|
| 1406 |
-
},
|
| 1407 |
-
"execution_count": 64,
|
| 1408 |
-
"metadata": {},
|
| 1409 |
-
"output_type": "execute_result"
|
| 1410 |
-
}
|
| 1411 |
-
],
|
| 1412 |
-
"source": [
|
| 1413 |
-
"len(df)"
|
| 1414 |
-
]
|
| 1415 |
-
},
|
| 1416 |
-
{
|
| 1417 |
-
"cell_type": "code",
|
| 1418 |
-
"execution_count": 65,
|
| 1419 |
-
"id": "3af855d3-a943-4574-985c-540d3f6b6f80",
|
| 1420 |
-
"metadata": {},
|
| 1421 |
-
"outputs": [
|
| 1422 |
-
{
|
| 1423 |
-
"data": {
|
| 1424 |
-
"text/plain": [
|
| 1425 |
-
"{'with_mean': True,\n",
|
| 1426 |
-
" 'with_std': True,\n",
|
| 1427 |
-
" 'copy': True,\n",
|
| 1428 |
-
" 'n_features_in_': 1,\n",
|
| 1429 |
-
" 'n_samples_seen_': 1836729,\n",
|
| 1430 |
-
" 'mean_': array([6.50604534]),\n",
|
| 1431 |
-
" 'var_': array([2.43319576]),\n",
|
| 1432 |
-
" 'scale_': array([1.55987043])}"
|
| 1433 |
-
]
|
| 1434 |
-
},
|
| 1435 |
-
"execution_count": 65,
|
| 1436 |
-
"metadata": {},
|
| 1437 |
-
"output_type": "execute_result"
|
| 1438 |
-
}
|
| 1439 |
-
],
|
| 1440 |
-
"source": [
|
| 1441 |
-
"scaler.__dict__"
|
| 1442 |
-
]
|
| 1443 |
-
},
|
| 1444 |
-
{
|
| 1445 |
-
"cell_type": "code",
|
| 1446 |
-
"execution_count": 66,
|
| 1447 |
-
"id": "15f8d5b9-37d5-453e-a6df-df6510cc5c81",
|
| 1448 |
-
"metadata": {},
|
| 1449 |
-
"outputs": [],
|
| 1450 |
-
"source": [
|
| 1451 |
-
"# output the normalization\n",
|
| 1452 |
-
"\n",
|
| 1453 |
-
"import json\n",
|
| 1454 |
-
"\n",
|
| 1455 |
-
"class NumpyEncoder(json.JSONEncoder):\n",
|
| 1456 |
-
" def default(self, obj):\n",
|
| 1457 |
-
" if isinstance(obj, np.ndarray):\n",
|
| 1458 |
-
" return obj.tolist()\n",
|
| 1459 |
-
" if isinstance(obj, np.int64):\n",
|
| 1460 |
-
" return int(obj)\n",
|
| 1461 |
-
" return json.JSONEncoder.default(self, obj)\n",
|
| 1462 |
-
" \n",
|
| 1463 |
-
"json.dump(scaler.__dict__,open('data/scaling.json','w'),cls=NumpyEncoder)"
|
| 1464 |
-
]
|
| 1465 |
-
},
|
| 1466 |
-
{
|
| 1467 |
-
"cell_type": "markdown",
|
| 1468 |
-
"id": "210b39d3-505b-4a6e-b186-35e660f4d510",
|
| 1469 |
-
"metadata": {},
|
| 1470 |
-
"source": [
|
| 1471 |
-
"**without KRAS**"
|
| 1472 |
-
]
|
| 1473 |
-
},
|
| 1474 |
-
{
|
| 1475 |
-
"cell_type": "code",
|
| 1476 |
-
"execution_count": 67,
|
| 1477 |
-
"id": "8dec95dc-a014-4d39-ae51-8de981173573",
|
| 1478 |
-
"metadata": {},
|
| 1479 |
-
"outputs": [],
|
| 1480 |
-
"source": [
|
| 1481 |
-
"smiles_sotorasib = 'C=CC(=O)N1CCN(c2nc(=O)n(-c3c(C)ccnc3C(C)C)c3nc(-c4c(O)cccc4F)c(F)cc23)[C@@H](C)C1'\n",
|
| 1482 |
-
"seq_kras_wt = 'MTEYKLVVVGAGGVGKSALTIQLIQNHFVDEYDPTIEDSYRKQVVIDGETCLLDILDTAGQEEYSAMRDQYMRTGEGFLCVFAINNTKSFEDIHHYREQIKRVKDSEDVPMVLVGNKCDLPSRTVDTKQAQDLARSYGIPFIETSAKTRQRVEDAFYTLVREIRQYRLKKISKEEKTPGCVKIKKCIIM'"
|
| 1483 |
-
]
|
| 1484 |
-
},
|
| 1485 |
-
{
|
| 1486 |
-
"cell_type": "code",
|
| 1487 |
-
"execution_count": 68,
|
| 1488 |
-
"id": "3f90eadd-d7e4-4104-961f-adaf5437e24b",
|
| 1489 |
-
"metadata": {},
|
| 1490 |
-
"outputs": [],
|
| 1491 |
-
"source": [
|
| 1492 |
-
"df_nokras = df[~df.seq.str.startswith(seq_kras_wt[:20])]"
|
| 1493 |
-
]
|
| 1494 |
-
},
|
| 1495 |
-
{
|
| 1496 |
-
"cell_type": "code",
|
| 1497 |
-
"execution_count": 69,
|
| 1498 |
-
"id": "f5f5335a-8f28-4058-8647-fcc8f7d2f841",
|
| 1499 |
-
"metadata": {},
|
| 1500 |
-
"outputs": [
|
| 1501 |
-
{
|
| 1502 |
-
"data": {
|
| 1503 |
-
"text/plain": [
|
| 1504 |
-
"1836326"
|
| 1505 |
-
]
|
| 1506 |
-
},
|
| 1507 |
-
"execution_count": 69,
|
| 1508 |
-
"metadata": {},
|
| 1509 |
-
"output_type": "execute_result"
|
| 1510 |
-
}
|
| 1511 |
-
],
|
| 1512 |
-
"source": [
|
| 1513 |
-
"len(df_nokras)"
|
| 1514 |
-
]
|
| 1515 |
-
},
|
| 1516 |
-
{
|
| 1517 |
-
"cell_type": "code",
|
| 1518 |
-
"execution_count": 10,
|
| 1519 |
-
"id": "47966268-c97c-4bd9-9c90-eb568249f2ef",
|
| 1520 |
-
"metadata": {},
|
| 1521 |
-
"outputs": [],
|
| 1522 |
-
"source": [
|
| 1523 |
-
"#df_nokras = df_nokras.astype({'affinity_uM': 'float32', 'neg_log10_affinity_M': 'float32', 'affinity': 'float32'})\n",
|
| 1524 |
-
"#df_nokras.to_parquet('data/all_nokras.parquet')\n",
|
| 1525 |
-
"#df_nokras = pd.read_parquet('data/all_nokras.parquet')"
|
| 1526 |
-
]
|
| 1527 |
-
},
|
| 1528 |
-
{
|
| 1529 |
-
"cell_type": "markdown",
|
| 1530 |
-
"id": "4838f164-aed7-4f2d-a047-df647dfb8ea6",
|
| 1531 |
-
"metadata": {},
|
| 1532 |
-
"source": [
|
| 1533 |
-
"**with covalently binding ligands only**"
|
| 1534 |
-
]
|
| 1535 |
-
},
|
| 1536 |
-
{
|
| 1537 |
-
"cell_type": "code",
|
| 1538 |
-
"execution_count": 89,
|
| 1539 |
-
"id": "c0d250a3-5680-446c-9c98-7d6623643304",
|
| 1540 |
-
"metadata": {},
|
| 1541 |
-
"outputs": [],
|
| 1542 |
-
"source": [
|
| 1543 |
-
"from rdkit.Chem import SDMolSupplier\n",
|
| 1544 |
-
"suppl = SDMolSupplier('data/CovPDB_ligands.sdf')\n"
|
| 1545 |
-
]
|
| 1546 |
-
},
|
| 1547 |
-
{
|
| 1548 |
-
"cell_type": "code",
|
| 1549 |
-
"execution_count": 90,
|
| 1550 |
-
"id": "0c7c0b26-1f2a-4b80-8117-f1e02719aac9",
|
| 1551 |
-
"metadata": {},
|
| 1552 |
-
"outputs": [
|
| 1553 |
-
{
|
| 1554 |
-
"name": "stderr",
|
| 1555 |
-
"output_type": "stream",
|
| 1556 |
-
"text": [
|
| 1557 |
-
"RDKit WARNING: [13:44:45] Warning: molecule is tagged as 3D, but all Z coords are zero\n",
|
| 1558 |
-
"RDKit WARNING: [13:44:45] Warning: molecule is tagged as 3D, but all Z coords are zero\n",
|
| 1559 |
-
"RDKit WARNING: [13:44:45] Warning: molecule is tagged as 3D, but all Z coords are zero\n",
|
| 1560 |
-
"RDKit WARNING: [13:44:45] Warning: molecule is tagged as 3D, but all Z coords are zero\n",
|
| 1561 |
-
"RDKit WARNING: [13:44:45] Warning: molecule is tagged as 3D, but all Z coords are zero\n",
|
| 1562 |
-
"RDKit WARNING: [13:44:45] Warning: molecule is tagged as 3D, but all Z coords are zero\n",
|
| 1563 |
-
"RDKit WARNING: [13:44:45] Warning: molecule is tagged as 3D, but all Z coords are zero\n",
|
| 1564 |
-
"RDKit WARNING: [13:44:45] Warning: molecule is tagged as 3D, but all Z coords are zero\n"
|
| 1565 |
-
]
|
| 1566 |
-
}
|
| 1567 |
-
],
|
| 1568 |
-
"source": [
|
| 1569 |
-
"from rdkit import Chem\n",
|
| 1570 |
-
"cov_smiles = [Chem.MolToSmiles(m) for m in suppl]"
|
| 1571 |
-
]
|
| 1572 |
-
},
|
| 1573 |
-
{
|
| 1574 |
-
"cell_type": "code",
|
| 1575 |
-
"execution_count": 74,
|
| 1576 |
-
"id": "258f593c-1cba-45cb-936e-8c1360075926",
|
| 1577 |
-
"metadata": {},
|
| 1578 |
-
"outputs": [],
|
| 1579 |
-
"source": [
|
| 1580 |
-
"df_cov = df[df['smiles'].isin(cov_smiles)]"
|
| 1581 |
-
]
|
| 1582 |
-
},
|
| 1583 |
-
{
|
| 1584 |
-
"cell_type": "code",
|
| 1585 |
-
"execution_count": 12,
|
| 1586 |
-
"id": "ee3fa0bc-9ad3-4ea7-9393-cbc7504f634c",
|
| 1587 |
-
"metadata": {},
|
| 1588 |
-
"outputs": [],
|
| 1589 |
-
"source": [
|
| 1590 |
-
"df_cov = df_cov.astype({'affinity_uM': 'float32', 'neg_log10_affinity_M': 'float32', 'affinity': 'float32'})\n",
|
| 1591 |
-
"#df_cov.reset_index(drop=True).to_parquet('data/cov.parquet')\n",
|
| 1592 |
-
"#df_cov = pd.read_parquet('data/cov.parquet')"
|
| 1593 |
-
]
|
| 1594 |
-
},
|
| 1595 |
-
{
|
| 1596 |
-
"cell_type": "code",
|
| 1597 |
-
"execution_count": 77,
|
| 1598 |
-
"id": "5c12fedc-4236-4587-a744-c0c9ec21ceaa",
|
| 1599 |
-
"metadata": {},
|
| 1600 |
-
"outputs": [
|
| 1601 |
-
{
|
| 1602 |
-
"data": {
|
| 1603 |
-
"text/plain": [
|
| 1604 |
-
"346"
|
| 1605 |
-
]
|
| 1606 |
-
},
|
| 1607 |
-
"execution_count": 77,
|
| 1608 |
-
"metadata": {},
|
| 1609 |
-
"output_type": "execute_result"
|
| 1610 |
-
}
|
| 1611 |
-
],
|
| 1612 |
-
"source": [
|
| 1613 |
-
"len(df_cov)"
|
| 1614 |
-
]
|
| 1615 |
-
},
|
| 1616 |
-
{
|
| 1617 |
-
"cell_type": "code",
|
| 1618 |
-
"execution_count": 78,
|
| 1619 |
-
"id": "1b73cea5-e6d9-427f-a31e-7ab38b5e6e4e",
|
| 1620 |
-
"metadata": {},
|
| 1621 |
-
"outputs": [
|
| 1622 |
-
{
|
| 1623 |
-
"data": {
|
| 1624 |
-
"text/plain": [
|
| 1625 |
-
"2.703125"
|
| 1626 |
-
]
|
| 1627 |
-
},
|
| 1628 |
-
"execution_count": 78,
|
| 1629 |
-
"metadata": {},
|
| 1630 |
-
"output_type": "execute_result"
|
| 1631 |
-
}
|
| 1632 |
-
],
|
| 1633 |
-
"source": [
|
| 1634 |
-
"346/128"
|
| 1635 |
-
]
|
| 1636 |
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},
|
| 1637 |
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{
|
| 1638 |
-
"cell_type": "code",
|
| 1639 |
-
"execution_count": 80,
|
| 1640 |
-
"id": "2d1d2955-7839-45e0-9a11-07dda2d51b24",
|
| 1641 |
-
"metadata": {},
|
| 1642 |
-
"outputs": [
|
| 1643 |
-
{
|
| 1644 |
-
"data": {
|
| 1645 |
-
"text/plain": [
|
| 1646 |
-
"<AxesSubplot:>"
|
| 1647 |
-
]
|
| 1648 |
-
},
|
| 1649 |
-
"execution_count": 80,
|
| 1650 |
-
"metadata": {},
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| 1651 |
-
"output_type": "execute_result"
|
| 1652 |
-
},
|
| 1653 |
-
{
|
| 1654 |
-
"data": {
|
| 1655 |
-
"image/png": 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w3iTHWbzb5x8l+UK3JXXqBHCiqn75ae4Ai2F/IXon8N2q+mFV/QK4H3h7xzWtB6eSbAboPZ4ex04N92UkCYvjqUer6lNd19O1qvpEVV1ZVVMsfln2laq6YM/OquoHwPeSvK43axvwzQ5L6tLTwPVJLum9b7ZxgX65fJYHgR295zuAB8axU/+D7OXdAHwAOJLkG715n6yqf+6uJK0zHwHu7d1H6TvABzuupxNV9WiSA8BjLP7K7HEusNsQJLkPmAGuSHICuBPYA+xPcjuLfwBvHkst3n5AktrjsIwkNchwl6QGGe6S1CDDXZIaZLhLUoMMd0lqkOEuSQ36fwQ+77qgfKxkAAAAAElFTkSuQmCC\n",
|
| 1656 |
-
"text/plain": [
|
| 1657 |
-
"<Figure size 432x288 with 1 Axes>"
|
| 1658 |
-
]
|
| 1659 |
-
},
|
| 1660 |
-
"metadata": {
|
| 1661 |
-
"needs_background": "light"
|
| 1662 |
-
},
|
| 1663 |
-
"output_type": "display_data"
|
| 1664 |
-
}
|
| 1665 |
-
],
|
| 1666 |
-
"source": [
|
| 1667 |
-
"df_cov['neg_log10_affinity_M'].hist()"
|
| 1668 |
-
]
|
| 1669 |
-
},
|
| 1670 |
-
{
|
| 1671 |
-
"cell_type": "code",
|
| 1672 |
-
"execution_count": 92,
|
| 1673 |
-
"id": "ab2e429b-d84c-42b7-b0dc-55e6728b9f81",
|
| 1674 |
-
"metadata": {},
|
| 1675 |
-
"outputs": [
|
| 1676 |
-
{
|
| 1677 |
-
"data": {
|
| 1678 |
-
"text/plain": [
|
| 1679 |
-
"167"
|
| 1680 |
-
]
|
| 1681 |
-
},
|
| 1682 |
-
"execution_count": 92,
|
| 1683 |
-
"metadata": {},
|
| 1684 |
-
"output_type": "execute_result"
|
| 1685 |
-
}
|
| 1686 |
-
],
|
| 1687 |
-
"source": [
|
| 1688 |
-
"len(df_cov['seq'].unique())"
|
| 1689 |
-
]
|
| 1690 |
-
},
|
| 1691 |
-
{
|
| 1692 |
-
"cell_type": "code",
|
| 1693 |
-
"execution_count": 94,
|
| 1694 |
-
"id": "4d535b74-1a32-45ee-a61f-f0cca805ad9b",
|
| 1695 |
-
"metadata": {},
|
| 1696 |
-
"outputs": [
|
| 1697 |
-
{
|
| 1698 |
-
"data": {
|
| 1699 |
-
"text/plain": [
|
| 1700 |
-
"49"
|
| 1701 |
-
]
|
| 1702 |
-
},
|
| 1703 |
-
"execution_count": 94,
|
| 1704 |
-
"metadata": {},
|
| 1705 |
-
"output_type": "execute_result"
|
| 1706 |
-
}
|
| 1707 |
-
],
|
| 1708 |
-
"source": [
|
| 1709 |
-
"len(df_cov['smiles'].unique())"
|
| 1710 |
-
]
|
| 1711 |
-
},
|
| 1712 |
-
{
|
| 1713 |
-
"cell_type": "code",
|
| 1714 |
-
"execution_count": 96,
|
| 1715 |
-
"id": "f59aa644-f848-447f-b82a-cff5b0507738",
|
| 1716 |
-
"metadata": {},
|
| 1717 |
-
"outputs": [
|
| 1718 |
-
{
|
| 1719 |
-
"data": {
|
| 1720 |
-
"text/plain": [
|
| 1721 |
-
"346"
|
| 1722 |
-
]
|
| 1723 |
-
},
|
| 1724 |
-
"execution_count": 96,
|
| 1725 |
-
"metadata": {},
|
| 1726 |
-
"output_type": "execute_result"
|
| 1727 |
-
}
|
| 1728 |
-
],
|
| 1729 |
-
"source": [
|
| 1730 |
-
"len(df_cov)"
|
| 1731 |
-
]
|
| 1732 |
-
},
|
| 1733 |
-
{
|
| 1734 |
-
"cell_type": "code",
|
| 1735 |
-
"execution_count": null,
|
| 1736 |
-
"id": "b8af53a6-5203-4889-9c4b-31140da5fc5b",
|
| 1737 |
-
"metadata": {},
|
| 1738 |
-
"outputs": [],
|
| 1739 |
-
"source": []
|
| 1740 |
-
}
|
| 1741 |
-
],
|
| 1742 |
-
"metadata": {
|
| 1743 |
-
"kernelspec": {
|
| 1744 |
-
"display_name": "Python 3 (ipykernel)",
|
| 1745 |
-
"language": "python",
|
| 1746 |
-
"name": "python3"
|
| 1747 |
-
},
|
| 1748 |
-
"language_info": {
|
| 1749 |
-
"codemirror_mode": {
|
| 1750 |
-
"name": "ipython",
|
| 1751 |
-
"version": 3
|
| 1752 |
-
},
|
| 1753 |
-
"file_extension": ".py",
|
| 1754 |
-
"mimetype": "text/x-python",
|
| 1755 |
-
"name": "python",
|
| 1756 |
-
"nbconvert_exporter": "python",
|
| 1757 |
-
"pygments_lexer": "ipython3",
|
| 1758 |
-
"version": "3.9.6"
|
| 1759 |
-
}
|
| 1760 |
-
},
|
| 1761 |
-
"nbformat": 4,
|
| 1762 |
-
"nbformat_minor": 5
|
| 1763 |
-
}
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