# -*- coding: utf-8 -*-
# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
# SPDX-License-Identifier: Apache-2.0
# Dataset for aromaticity prediction
import pandas as pd
from dgl.data.utils import get_download_dir, download, _get_dgl_url
from .csv_dataset import MoleculeCSVDataset
from ..utils.mol_to_graph import smiles_to_bigraph
__all__ = ['PubChemBioAssayAromaticity']
"""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
<https://www.ncbi.nlm.nih.gov/pubmed/31408336>`__ 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.
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)
df, smiles_to_graph, node_featurizer, edge_featurizer, "cano_smiles",
'./pubchem_aromaticity_dglgraph.bin', load=load, log_every=log_every, n_jobs=n_jobs)