Source code for

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

import numpy as np
import os
import os.path as osp
import pandas as pd
import pathlib
import zipfile

from collections import defaultdict
from dgl import backend as F
from import download, get_download_dir, _get_dgl_url, save_graphs, load_graphs
from rdkit import Chem
from rdkit.Chem import ChemicalFeatures
from rdkit import RDConfig

from ..utils.mol_to_graph import mol_to_complete_graph
from ..utils.featurizers import atom_type_one_hot, atom_hybridization_one_hot, atom_is_aromatic

__all__ = ['TencentAlchemyDataset']

def alchemy_nodes(mol):
    """Featurization for all atoms in a molecule. The atom indices
    will be preserved.

    mol : rdkit.Chem.rdchem.Mol
        RDKit molecule object

    atom_feats_dict : dict
        Dictionary for atom features
    atom_feats_dict = defaultdict(list)
    is_donor = defaultdict(int)
    is_acceptor = defaultdict(int)

    fdef_name = osp.join(RDConfig.RDDataDir, 'BaseFeatures.fdef')
    mol_featurizer = ChemicalFeatures.BuildFeatureFactory(fdef_name)
    mol_feats = mol_featurizer.GetFeaturesForMol(mol)
    mol_conformers = mol.GetConformers()
    assert len(mol_conformers) == 1

    for i in range(len(mol_feats)):
        if mol_feats[i].GetFamily() == 'Donor':
            node_list = mol_feats[i].GetAtomIds()
            for u in node_list:
                is_donor[u] = 1
        elif mol_feats[i].GetFamily() == 'Acceptor':
            node_list = mol_feats[i].GetAtomIds()
            for u in node_list:
                is_acceptor[u] = 1

    num_atoms = mol.GetNumAtoms()
    for u in range(num_atoms):
        atom = mol.GetAtomWithIdx(u)
        atom_type = atom.GetAtomicNum()
        num_h = atom.GetTotalNumHs()

        h_u = []
        h_u += atom_type_one_hot(atom, ['H', 'C', 'N', 'O', 'F', 'S', 'Cl'])
        h_u += atom_is_aromatic(atom)
        h_u += atom_hybridization_one_hot(atom, [Chem.rdchem.HybridizationType.SP,

    atom_feats_dict['n_feat'] = F.stack(atom_feats_dict['n_feat'], dim=0)
    atom_feats_dict['node_type'] = F.tensor(np.array(

    return atom_feats_dict

def alchemy_edges(mol, self_loop=False):
    """Featurization for all bonds in a molecule.
    The bond indices will be preserved.

    mol : rdkit.Chem.rdchem.Mol
        RDKit molecule object
    self_loop : bool
        Whether to add self loops. Default to be False.

    bond_feats_dict : dict
        Dictionary for bond features
    bond_feats_dict = defaultdict(list)

    mol_conformers = mol.GetConformers()
    assert len(mol_conformers) == 1
    geom = mol_conformers[0].GetPositions()

    num_atoms = mol.GetNumAtoms()
    for u in range(num_atoms):
        for v in range(num_atoms):
            if u == v and not self_loop:

            e_uv = mol.GetBondBetweenAtoms(u, v)
            if e_uv is None:
                bond_type = None
                bond_type = e_uv.GetBondType()
                float(bond_type == x)
                for x in (Chem.rdchem.BondType.SINGLE,
                          Chem.rdchem.BondType.AROMATIC, None)
                np.linalg.norm(geom[u] - geom[v]))

    bond_feats_dict['e_feat'] = F.tensor(
    bond_feats_dict['distance'] = F.tensor(
        np.array(bond_feats_dict['distance']).astype(np.float32)).reshape(-1 , 1)

    return bond_feats_dict

[docs]class TencentAlchemyDataset(object): """ Developed by the Tencent Quantum Lab, the dataset lists 12 quantum mechanical properties of 130, 000+ organic molecules, comprising up to 12 heavy atoms (C, N, O, S, F and Cl), sampled from the GDBMedChem database. These properties have been calculated using the open-source computational chemistry program Python-based Simulation of Chemistry Framework (PySCF). For more details, check the `paper <>`__. Parameters ---------- mode : str 'dev', 'valid' or 'test', separately for training, validation and test. Default to be 'dev'. Note that 'test' is not available as the alchemy contest is ongoing. mol_to_graph: callable, str -> DGLGraph A function turning an RDKit molecule instance into a DGLGraph. Default to :func:`dgllife.utils.mol_to_complete_graph`. 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. By default, we construct graphs where nodes represent atoms and node features represent atom features. We store the atomic numbers under the name ``"node_type"`` and store the atom features under the name ``"n_feat"``. The atom features include: * One hot encoding for atom types * Atomic number of atoms * Whether the atom is a donor * Whether the atom is an acceptor * Whether the atom is aromatic * One hot encoding for atom hybridization * Total number of Hs on the atom 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. By default, we construct edges between every pair of atoms, excluding the self loops. We store the distance between the end atoms under the name ``"distance"`` and store the edge features under the name ``"e_feat"``. The edge features represent one hot encoding of edge types (bond types and non-bond edges). 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 preprocess from scratch. Default to True. """ def __init__(self, mode='dev', mol_to_graph=mol_to_complete_graph, node_featurizer=alchemy_nodes, edge_featurizer=alchemy_edges, load=True): if mode == 'test': raise ValueError('The test mode is not supported before ' 'the alchemy contest finishes.') assert mode in ['dev', 'valid', 'test'], \ 'Expect mode to be dev, valid or test, got {}.'.format(mode) self.mode = mode # Construct DGLGraphs from raw data or use the preprocessed data self.load = load file_dir = osp.join(get_download_dir(), 'Alchemy_data') if load: file_name = "{}_processed_dgl".format(mode) else: file_name = "{}_single_sdf".format(mode) self.file_dir = pathlib.Path(file_dir, file_name) self._url = 'dataset/alchemy/' self.zip_file_path = pathlib.Path(file_dir, file_name + '.zip') download(_get_dgl_url(self._url + file_name + '.zip'), path=str(self.zip_file_path), overwrite=False) if not os.path.exists(str(self.file_dir)): archive = zipfile.ZipFile(self.zip_file_path) archive.extractall(file_dir) archive.close() self._load(mol_to_graph, node_featurizer, edge_featurizer) def _load(self, mol_to_graph, node_featurizer, edge_featurizer): if self.load: self.graphs, label_dict = load_graphs(osp.join(self.file_dir, "{}_graphs.bin".format(self.mode))) self.labels = label_dict['labels'] with open(osp.join(self.file_dir, "{}_smiles.txt".format(self.mode)), 'r') as f: smiles_ = f.readlines() self.smiles = [s.strip() for s in smiles_] else: print('Start preprocessing dataset...') target_file = pathlib.Path(self.file_dir, "{}_target.csv".format(self.mode)) = pd.read_csv( target_file, index_col=0, usecols=['gdb_idx',] + ['property_{:d}'.format(x) for x in range(12)]) =[['property_{:d}'.format(x) for x in range(12)]] self.graphs, self.labels, self.smiles = [], [], [] supp = Chem.SDMolSupplier(osp.join(self.file_dir, self.mode + ".sdf")) cnt = 0 dataset_size = len( for mol, label in zip(supp, cnt += 1 print('Processing molecule {:d}/{:d}'.format(cnt, dataset_size)) graph = mol_to_graph(mol, node_featurizer=node_featurizer, edge_featurizer=edge_featurizer) smiles = Chem.MolToSmiles(mol) self.smiles.append(smiles) self.graphs.append(graph) label = F.tensor(np.array(label[1].tolist()).astype(np.float32)) self.labels.append(label) save_graphs(osp.join(self.file_dir, "{}_graphs.bin".format(self.mode)), self.graphs, labels={'labels': F.stack(self.labels, dim=0)}) with open(osp.join(self.file_dir, "{}_smiles.txt".format(self.mode)), 'w') as f: for s in self.smiles: f.write(s + '\n') self.set_mean_and_std() print(len(self.graphs), "loaded!")
[docs] def __getitem__(self, item): """Get datapoint with index Parameters ---------- item : int Datapoint index Returns ------- str SMILES for the ith datapoint DGLGraph DGLGraph for the ith datapoint Tensor of dtype float32 and shape (T) Labels of the datapoint for all tasks. """ return self.smiles[item], self.graphs[item], self.labels[item]
[docs] def __len__(self): """Size for the dataset. Returns ------- int Size for the dataset. """ return len(self.graphs)
[docs] def set_mean_and_std(self, mean=None, std=None): """Set mean and std or compute from labels for future normalization. The mean and std can be fetched later with ``self.mean`` and ``self.std``. Parameters ---------- mean : float32 tensor of shape (T) Mean of labels for all tasks. std : float32 tensor of shape (T) Std of labels for all tasks. """ labels = np.array([i.numpy() for i in self.labels]) if mean is None: mean = np.mean(labels, axis=0) if std is None: std = np.std(labels, axis=0) self.mean = mean self.std = std