dmlc--dgl
Mufei Li
50 行
2.3 KiB
Python
50 行
2.3 KiB
Python
"""Dataset for aromaticity prediction"""
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import pandas as pd
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from dgl.data.utils import get_download_dir, download, _get_dgl_url
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from .csv_dataset import MoleculeCSVDataset
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from ..utils.mol_to_graph import smiles_to_bigraph
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__all__ = ['PubChemBioAssayAromaticity']
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class PubChemBioAssayAromaticity(MoleculeCSVDataset):
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"""Subset of PubChem BioAssay Dataset for aromaticity prediction.
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The dataset was constructed in `Pushing the Boundaries of Molecular Representation for Drug
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Discovery with the Graph Attention Mechanism
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<https://www.ncbi.nlm.nih.gov/pubmed/31408336>`__ and is accompanied by the task of predicting
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the number of aromatic atoms in molecules.
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The dataset was constructed by sampling 3945 molecules with 0-40 aromatic atoms from the
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PubChem BioAssay dataset.
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Parameters
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----------
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smiles_to_graph: callable, str -> DGLGraph
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A function turning smiles into a DGLGraph.
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Default to :func:`dgllife.utils.smiles_to_bigraph`.
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node_featurizer : callable, rdkit.Chem.rdchem.Mol -> dict
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Featurization for nodes like atoms in a molecule, which can be used to update
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ndata for a DGLGraph. Default to None.
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edge_featurizer : callable, rdkit.Chem.rdchem.Mol -> dict
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Featurization for edges like bonds in a molecule, which can be used to update
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edata for a DGLGraph. Default to None.
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load : bool
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Whether to load the previously pre-processed dataset or pre-process from scratch.
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``load`` should be False when we want to try different graph construction and
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featurization methods and need to pre-process from scratch. Default to True.
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log_every : bool
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Print a message every time ``log_every`` molecules are processed. Default to 1000.
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"""
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def __init__(self, smiles_to_graph=smiles_to_bigraph,
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node_featurizer=None, edge_featurizer=None, load=True, log_every=1000):
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self._url = 'dataset/pubchem_bioassay_aromaticity.csv'
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data_path = get_download_dir() + '/pubchem_bioassay_aromaticity.csv'
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download(_get_dgl_url(self._url), path=data_path)
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df = pd.read_csv(data_path)
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super(PubChemBioAssayAromaticity, self).__init__(
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df, smiles_to_graph, node_featurizer, edge_featurizer, "cano_smiles",
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"pubchem_aromaticity_dglgraph.bin", load=load, log_every=log_every)
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