Source code for

# -*- coding: utf-8 -*-
# Copyright, Inc. or its affiliates. All Rights Reserved.
# SPDX-License-Identifier: Apache-2.0
# Dataset for aromaticity prediction

import pandas as pd

from import get_download_dir, download, _get_dgl_url

from .csv_dataset import MoleculeCSVDataset
from ..utils.mol_to_graph import smiles_to_bigraph

__all__ = ['PubChemBioAssayAromaticity']

[docs]class PubChemBioAssayAromaticity(MoleculeCSVDataset): """Subset of PubChem BioAssay Dataset for aromaticity prediction. The dataset was constructed in `Pushing the Boundaries of Molecular Representation for Drug Discovery with the Graph Attention Mechanism <>`__ and is accompanied by the task of predicting the number of aromatic atoms in molecules. The dataset was constructed by sampling 3945 molecules with 0-40 aromatic atoms from the PubChem BioAssay dataset. Parameters ---------- smiles_to_graph: callable, str -> DGLGraph A function turning a SMILES string into a DGLGraph. Default to :func:`dgllife.utils.smiles_to_bigraph`. node_featurizer : callable, rdkit.Chem.rdchem.Mol -> dict Featurization for nodes like atoms in a molecule, which can be used to update ndata for a DGLGraph. Default to None. edge_featurizer : callable, rdkit.Chem.rdchem.Mol -> dict Featurization for edges like bonds in a molecule, which can be used to update edata for a DGLGraph. Default to None. load : bool Whether to load the previously pre-processed dataset or pre-process from scratch. ``load`` should be False when we want to try different graph construction and featurization methods and need to pre-process from scratch. Default to False. log_every : bool Print a message every time ``log_every`` molecules are processed. Default to 1000. n_jobs : int The maximum number of concurrently running jobs for graph construction and featurization, using joblib backend. Default to 1. """ def __init__(self, smiles_to_graph=smiles_to_bigraph, node_featurizer=None, edge_featurizer=None, load=False, log_every=1000, n_jobs=1): self._url = 'dataset/pubchem_bioassay_aromaticity.csv' data_path = get_download_dir() + '/pubchem_bioassay_aromaticity.csv' download(_get_dgl_url(self._url), path=data_path, overwrite=False) df = pd.read_csv(data_path) super(PubChemBioAssayAromaticity, self).__init__( df, smiles_to_graph, node_featurizer, edge_featurizer, "cano_smiles", './pubchem_aromaticity_dglgraph.bin', load=load, log_every=log_every, n_jobs=n_jobs)