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.. PROTAC-Degradation-Predictor documentation master file, created by
sphinx-quickstart on Mon Aug 23 17:31:15 2021.
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===========================================================
PROTAC-Degradation-Predictor: Documentation and Overview
===========================================================
**PROTAC-Degradation-Predictor** is a Python package designed to predict the activity of PROTAC molecules using advanced machine learning techniques. The tool aims to assist researchers in evaluating the potential effectiveness of PROTACs, a novel class of drugs that target protein degradation.
The package Github repository can be found `here <https://github.com/ribesstefano/PROTAC-Degradation-Predictor.git>`_.
.. .. image:: https://yourimageurl.com/logo.png # Add your project's logo or any relevant image
.. :align: center
Introduction
============
PROTACs (Proteolysis Targeting Chimeras) are a class of molecules that induce the degradation of specific proteins. This package allows researchers to predict the activity of PROTACs by leveraging a variety of machine learning models, including XGBoost and PyTorch-based pretrained neural networks.
The primary functionalities of this package include:
- Predicting PROTAC activity using different machine learning models.
- Accessing curated datasets for training and evaluation.
- Hyperparameter tuning and model training using Optuna.
Features
========
- **Machine Learning Models**: Utilize XGBoost, PyTorch, and scikit-learn models to predict PROTAC activity (refer to the :func:`protac_degradation_predictor.protac_degradation_predictor.get_protac_active_proba` function).
- **Dataset Handling**: Load and manage datasets specific to PROTAC research (refer to the :func:`protac_degradation_predictor.data_utils.load_curated_dataset` function).
- **Customizability**: Tune model hyperparameters and experiment with different model configurations (refer to the :func:`protac_degradation_predictor.optuna_utils.hyperparameter_tuning_and_training` function).
Quickstart
==========
To get started with PROTAC-Degradation-Predictor, follow these steps:
1. **Installation**:
Install the package using pip:
.. code-block:: bash
pip install git+https://github.com/ribesstefano/PROTAC-Degradation-Predictor.git
2. **Basic Usage**:
Here's an example of how to predict PROTAC activity:
.. code-block:: python
from protac_degradation_predictor import get_protac_active_proba
smiles = "CC(C)C1=CC=C(C=C1)C2=NC3=CC=CC=C3C(=O)N2"
e3_ligase = "Q9Y6K9"
target_uniprot = "P04637"
cell_line = "HCT116"
prediction = get_protac_active_proba(
protac_smiles=smiles,
e3_ligase=e3_ligase,
target_uniprot=target_uniprot,
cell_line=cell_line,
device='cpu',
use_models_from_cv=False,
use_xgboost_models=True,
study_type='standard'
)
print(prediction)
For more detailed usage and customization, refer to the relevant sections below.
Contents
========
.. toctree::
:maxdepth: 2
:caption: Documentation Contents:
source/modules
source/protac_degradation_predictor
source/protac_degradation_predictor.optuna_utils
source/protac_degradation_predictor.protac_dataset
source/protac_degradation_predictor.pytorch_models
Getting Help
============
If you encounter any issues or have questions, please refer to the following resources:
- **Documentation**: Full API documentation and user guide.
- **GitHub Issues**: Report bugs or request features on the `GitHub Issues <https://github.com/ribesstefano/PROTAC-Degradation-Predictor/issues>`_ page.
- **Contributing**: Learn how to contribute to the project by reading our `Contribution Guidelines <https://github.com/ribesstefano/PROTAC-Degradation-Predictor/blob/main/CONTRIBUTING.md>`_.
License
=======
This project is licensed under the MIT License. See the `LICENSE <https://github.com/ribesstefano/PROTAC-Degradation-Predictor/blob/main/LICENSE>`_ file for details.
About
=====
**Author**: Stefano Ribes
**Version**: v1.0.2
Built with Sphinx using the `Read the Docs theme <https://sphinx-rtd-theme.readthedocs.io/>`_.
----------
*This documentation was last updated on August 27, 2024.*
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