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import csv
import json
import os
import datasets
_CITATION = """
@inproceedings{gebhard2022inferring,
title={Inferring molecular complexity from mass spectrometry data using machine learning},
author={Gebhard, Timothy D and Bell, Aaron C and Gong, Jian and Hastings, Jaden J. A. and Fricke, G. Matthew and Cabrol, Nathalie and Sandford, Scott and Phillips, Michael and Warren-Rhodes, Kimberley and Baydin, Atilim Gunes},
booktitle={NeurIPS Workshop on Machine Learning and the Physical Sciences},
year={2022}
}
"""
_DESCRIPTION = """
SaganMC is a molecular dataset designed to support machine learning research in molecular complexity inference. It includes over 400,000 molecules with computed structural, physico-chemical, and complexity descriptors, and a subset of ~16k molecules that additionally include experimental mass spectra.
"""
_HOMEPAGE = "https://huggingface.co/datasets/oxai4science/sagan-mc"
_LICENSE = "CC-BY-4.0"
_URLS = {
"sagan-mc-400k": "https://huggingface.co/datasets/oxai4science/sagan-mc/resolve/main/sagan-mc-400k.csv",
"sagan-mc-spectra-16k": "https://huggingface.co/datasets/oxai4science/sagan-mc/resolve/main/sagan-mc-spectra-16k.csv",
}
class SaganMC(datasets.GeneratorBasedBuilder):
VERSION = datasets.Version("1.0.0")
BUILDER_CONFIGS = [
datasets.BuilderConfig(name="sagan-mc-400k", version=VERSION, description="Full dataset with ~400k molecules"),
datasets.BuilderConfig(name="sagan-mc-spectra-16k", version=VERSION, description="Subset with mass spectra (~16k molecules)"),
]
DEFAULT_CONFIG_NAME = "sagan-mc-400k"
def _info(self):
features = datasets.Features({
"inchi": datasets.Value("string"),
"inchikey": datasets.Value("string"),
"selfies": datasets.Value("string"),
"smiles": datasets.Value("string"),
"smiles_scaffold": datasets.Value("string"),
"formula": datasets.Value("string"),
"fingerprint_morgan": datasets.Value("string"),
"num_atoms": datasets.Value("int32"),
"num_atoms_all": datasets.Value("int32"),
"num_bonds": datasets.Value("int32"),
"num_bonds_all": datasets.Value("int32"),
"num_rings": datasets.Value("int32"),
"num_aromatic_rings": datasets.Value("int32"),
"physchem_mol_weight": datasets.Value("float"),
"physchem_logp": datasets.Value("float"),
"physchem_tpsa": datasets.Value("float"),
"physchem_qed": datasets.Value("float"),
"physchem_h_acceptors": datasets.Value("int32"),
"physchem_h_donors": datasets.Value("int32"),
"physchem_rotatable_bonds": datasets.Value("int32"),
"physchem_fraction_csp3": datasets.Value("float"),
"mass_spectrum_nist": datasets.Value("string"),
"complex_ma_score": datasets.Value("int32"),
"complex_ma_runtime": datasets.Value("float"),
"complex_bertz_score": datasets.Value("float"),
"complex_bertz_runtime": datasets.Value("float"),
"complex_boettcher_score": datasets.Value("float"),
"complex_boettcher_runtime": datasets.Value("float"),
"synth_sa_score": datasets.Value("float"),
"meta_cas_number": datasets.Value("string"),
"meta_names": datasets.Value("string"),
"meta_iupac_name": datasets.Value("string"),
"meta_comment": datasets.Value("string"),
"meta_origin": datasets.Value("string"),
"meta_reference": datasets.Value("string"),
"split": datasets.ClassLabel(names=["train", "val", "test"])
})
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
url = _URLS[self.config.name]
data_path = dl_manager.download_and_extract(url)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"filepath": data_path, "split_name": "train"},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={"filepath": data_path, "split_name": "val"},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={"filepath": data_path, "split_name": "test"},
),
]
def _generate_examples(self, filepath, split_name):
numeric_fields = [
"num_atoms", "num_atoms_all", "num_bonds", "num_bonds_all", "num_rings", "num_aromatic_rings",
"physchem_mol_weight", "physchem_logp", "physchem_tpsa", "physchem_qed",
"physchem_h_acceptors", "physchem_h_donors", "physchem_rotatable_bonds", "physchem_fraction_csp3",
"complex_ma_score", "complex_ma_runtime", "complex_bertz_score", "complex_bertz_runtime",
"complex_boettcher_score", "complex_boettcher_runtime", "synth_sa_score"
]
with open(filepath, encoding="utf-8") as f:
reader = csv.DictReader(f)
for idx, row in enumerate(reader):
if row["split"] == split_name:
for field in numeric_fields:
if field in row and row[field] == "":
row[field] = None
yield idx, row
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