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dmlc--dgl
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Mufei Li d3560b7155 [DGL-LifeSci] Documentation (#1414) 
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"""USPTO for reaction prediction"""
import numpy as np
import os
import torch
from collections import defaultdict
from dgl.data.utils import get_download_dir, download, _get_dgl_url, extract_archive, \
save_graphs, load_graphs
from functools import partial
from rdkit import Chem, RDLogger
from rdkit.Chem import rdmolops
from tqdm import tqdm
from ..utils.featurizers import BaseAtomFeaturizer, ConcatFeaturizer, atom_type_one_hot, \
atom_degree_one_hot, atom_explicit_valence_one_hot, atom_implicit_valence_one_hot, \
atom_is_aromatic, BaseBondFeaturizer, bond_type_one_hot, bond_is_conjugated, bond_is_in_ring
from ..utils.mol_to_graph import mol_to_bigraph, mol_to_complete_graph
__all__ = ['WLNReactionDataset',
'USPTO']
# Disable RDKit warnings
RDLogger.DisableLog('rdApp.*')
# Atom types distinguished in featurization
atom_types = ['C', 'N', 'O', 'S', 'F', 'Si', 'P', 'Cl', 'Br', 'Mg', 'Na', 'Ca', 'Fe',
'As', 'Al', 'I', 'B', 'V', 'K', 'Tl', 'Yb', 'Sb', 'Sn', 'Ag', 'Pd', 'Co',
'Se', 'Ti', 'Zn', 'H', 'Li', 'Ge', 'Cu', 'Au', 'Ni', 'Cd', 'In', 'Mn', 'Zr',
'Cr', 'Pt', 'Hg', 'Pb', 'W', 'Ru', 'Nb', 'Re', 'Te', 'Rh', 'Tc', 'Ba', 'Bi',
'Hf', 'Mo', 'U', 'Sm', 'Os', 'Ir', 'Ce', 'Gd', 'Ga', 'Cs']
default_node_featurizer = BaseAtomFeaturizer({
'hv': ConcatFeaturizer(
[partial(atom_type_one_hot, allowable_set=atom_types, encode_unknown=True),
partial(atom_degree_one_hot, allowable_set=list(range(6))),
atom_explicit_valence_one_hot,
partial(atom_implicit_valence_one_hot, allowable_set=list(range(6))),
atom_is_aromatic]
)
})
default_edge_featurizer = BaseBondFeaturizer({
'he': ConcatFeaturizer([
bond_type_one_hot, bond_is_conjugated, bond_is_in_ring]
)
})
def default_atom_pair_featurizer(reactants):
"""Featurize each pair of atoms, which will be used in updating
the edata of a complete DGLGraph.
The features include the bond type between the atoms (if any) and whether
they belong to the same molecule. It is used in the global attention mechanism.
Parameters
----------
reactants : str
SMILES for reactants
data_field : str
Key for storing the features in DGLGraph.edata. Default to 'atom_pair'
Returns
-------
float32 tensor of shape (V^2, 10)
features for each pair of atoms.
"""
# Decide the reactant membership for each atom
atom_to_reactant = dict()
reactant_list = reactants.split('.')
for id, s in enumerate(reactant_list):
mol = Chem.MolFromSmiles(s)
for atom in mol.GetAtoms():
atom_to_reactant[atom.GetIntProp('molAtomMapNumber') - 1] = id
# Construct mapping from atom pair to RDKit bond object
all_reactant_mol = Chem.MolFromSmiles(reactants)
atom_pair_to_bond = dict()
for bond in all_reactant_mol.GetBonds():
atom1 = bond.GetBeginAtom().GetIntProp('molAtomMapNumber') - 1
atom2 = bond.GetEndAtom().GetIntProp('molAtomMapNumber') - 1
atom_pair_to_bond[(atom1, atom2)] = bond
atom_pair_to_bond[(atom2, atom1)] = bond
def _featurize_a_bond(bond):
return bond_type_one_hot(bond) + bond_is_conjugated(bond) + bond_is_in_ring(bond)
features = []
num_atoms = all_reactant_mol.GetNumAtoms()
for i in range(num_atoms):
for j in range(num_atoms):
pair_feature = np.zeros(10)
if i == j:
features.append(pair_feature)
continue
bond = atom_pair_to_bond.get((i, j), None)
if bond is not None:
pair_feature[1:7] = _featurize_a_bond(bond)
else:
pair_feature[0] = 1.
pair_feature[-4] = 1. if atom_to_reactant[i] != atom_to_reactant[j] else 0.
pair_feature[-3] = 1. if atom_to_reactant[i] == atom_to_reactant[j] else 0.
pair_feature[-2] = 1. if len(reactant_list) == 1 else 0.
pair_feature[-1] = 1. if len(reactant_list) > 1 else 0.
features.append(pair_feature)
return torch.from_numpy(np.stack(features, axis=0).astype(np.float32))
def get_pair_label(reactants_mol, graph_edits):
"""Construct labels for each pair of atoms in reaction center prediction
Parameters
----------
reactants_mol : rdkit.Chem.rdchem.Mol
RDKit molecule instance for all reactants in a reaction
graph_edits : str
Specifying which pairs of atoms loss a bond or form a particular bond in the reaction
Returns
-------
float32 tensor of shape (V^2, 5)
Labels constructed. V for the number of atoms in the reactants.
"""
# 0 for losing the bond
# 1, 2, 3, 1.5 separately for forming a single, double, triple or aromatic bond.
bond_change_to_id = {0.0: 0, 1:1, 2:2, 3:3, 1.5:4}
pair_to_changes = defaultdict(list)
for edit in graph_edits.split(';'):
a1, a2, change = edit.split('-')
atom1 = int(a1) - 1
atom2 = int(a2) - 1
change = bond_change_to_id[float(change)]
pair_to_changes[(atom1, atom2)].append(change)
pair_to_changes[(atom2, atom1)].append(change)
num_atoms = reactants_mol.GetNumAtoms()
labels = torch.zeros((num_atoms, num_atoms, 5))
for pair in pair_to_changes.keys():
i, j = pair
labels[i, j, pair_to_changes[(j, i)]] = 1.
return labels.reshape(-1, 5)
def get_bond_changes(reaction):
"""Get the bond changes in a reaction.
Parameters
----------
reaction : str
SMILES for a reaction, e.g. [CH3:14][NH2:15].[N+:1](=[O:2])([O-:3])[c:4]1[cH:5][c:6]([C:7]
(=[O:8])[OH:9])[cH:10][cH:11][c:12]1[Cl:13].[OH2:16]>>[N+:1](=[O:2])([O-:3])[c:4]1[cH:5]
[c:6]([C:7](=[O:8])[OH:9])[cH:10][cH:11][c:12]1[NH:15][CH3:14]. It consists of reactants,
products and the atom mapping.
Returns
-------
bond_changes : set of 3-tuples
Each tuple consists of (atom1, atom2, change type)
There are 5 possible values for change type. 0 for losing the bond, and 1, 2, 3, 1.5
separately for forming a single, double, triple or aromatic bond.
"""
reactants = Chem.MolFromSmiles(reaction.split('>')[0])
products = Chem.MolFromSmiles(reaction.split('>')[2])
conserved_maps = [
a.GetProp('molAtomMapNumber')
for a in products.GetAtoms() if a.HasProp('molAtomMapNumber')]
bond_changes = set() # keep track of bond changes
# Look at changed bonds
bonds_prev = {}
for bond in reactants.GetBonds():
nums = sorted(
[bond.GetBeginAtom().GetProp('molAtomMapNumber'),
bond.GetEndAtom().GetProp('molAtomMapNumber')])
if (nums[0] not in conserved_maps) and (nums[1] not in conserved_maps):
continue
bonds_prev['{}~{}'.format(nums[0], nums[1])] = bond.GetBondTypeAsDouble()
bonds_new = {}
for bond in products.GetBonds():
nums = sorted(
[bond.GetBeginAtom().GetProp('molAtomMapNumber'),
bond.GetEndAtom().GetProp('molAtomMapNumber')])
bonds_new['{}~{}'.format(nums[0], nums[1])] = bond.GetBondTypeAsDouble()
for bond in bonds_prev:
if bond not in bonds_new:
# lost bond
bond_changes.add((bond.split('~')[0], bond.split('~')[1], 0.0))
else:
if bonds_prev[bond] != bonds_new[bond]:
# changed bond
bond_changes.add((bond.split('~')[0], bond.split('~')[1], bonds_new[bond]))
for bond in bonds_new:
if bond not in bonds_prev:
# new bond
bond_changes.add((bond.split('~')[0], bond.split('~')[1], bonds_new[bond]))
return bond_changes
def process_file(path):
"""Pre-process a file of reactions for working with WLN.
Parameters
----------
path : str
Path to the file of reactions
"""
with open(path, 'r') as input_file, open(path + '.proc', 'w') as output_file:
for line in tqdm(input_file):
reaction = line.strip()
bond_changes = get_bond_changes(reaction)
output_file.write('{} {}\n'.format(
reaction,
';'.join(['{}-{}-{}'.format(x[0], x[1], x[2]) for x in bond_changes])))
print('Finished processing {}'.format(path))
class WLNReactionDataset(object):
"""Dataset for reaction prediction with WLN
Parameters
----------
raw_file_path : str
Path to the raw reaction file, where each line is the SMILES for a reaction.
We will check if raw_file_path + '.proc' exists, where each line has the reaction
SMILES and the corresponding graph edits. If not, we will preprocess
the raw reaction file.
mol_graph_path : str
Path to save/load DGLGraphs for molecules.
mol_to_graph: callable, str -> DGLGraph
A function turning RDKit molecule instances into DGLGraphs.
Default to :func:`dgllife.utils.mol_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. By default, we consider descriptors including atom type,
atom degree, atom explicit valence, atom implicit valence, aromaticity.
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 consider descriptors including bond type,
whether bond is conjugated and whether bond is in ring.
atom_pair_featurizer : callable, str -> dict
Featurization for each pair of atoms in multiple reactants. The result will be
used to update edata in the complete DGLGraphs. By default, the features include
the bond type between the atoms (if any) and whether they belong to the same molecule.
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,
raw_file_path,
mol_graph_path,
mol_to_graph=mol_to_bigraph,
node_featurizer=default_node_featurizer,
edge_featurizer=default_edge_featurizer,
atom_pair_featurizer=default_atom_pair_featurizer,
load=True):
super(WLNReactionDataset, self).__init__()
self._atom_pair_featurizer = atom_pair_featurizer
self.atom_pair_features = []
self.atom_pair_labels = []
# Map number of nodes to a corresponding complete graph
self.complete_graphs = dict()
path_to_reaction_file = raw_file_path + '.proc'
if not os.path.isfile(path_to_reaction_file):
# Pre-process graph edits information
process_file(raw_file_path)
full_mols, full_reactions, full_graph_edits = \
self.load_reaction_data(path_to_reaction_file)
if load and os.path.isfile(mol_graph_path):
self.reactant_mol_graphs, _ = load_graphs(mol_graph_path)
else:
self.reactant_mol_graphs = []
for i in range(len(full_mols)):
if i % 10000 == 0:
print('Processing reaction {:d}/{:d}'.format(i + 1, len(full_mols)))
mol = full_mols[i]
reactant_mol_graph = mol_to_graph(mol, node_featurizer=node_featurizer,
edge_featurizer=edge_featurizer,
canonical_atom_order=False)
self.reactant_mol_graphs.append(reactant_mol_graph)
save_graphs(mol_graph_path, self.reactant_mol_graphs)
self.mols = full_mols
self.reactions = full_reactions
self.graph_edits = full_graph_edits
self.atom_pair_features.extend([None for _ in range(len(self.mols))])
self.atom_pair_labels.extend([None for _ in range(len(self.mols))])
def load_reaction_data(self, file_path):
"""Load reaction data from the raw file.
Parameters
----------
file_path : str
Path to read the file.
Returns
-------
all_mols : list of rdkit.Chem.rdchem.Mol
RDKit molecule instances
all_reactions : list of str
Reactions
all_graph_edits : list of str
Graph edits in the reactions.
"""
all_mols = []
all_reactions = []
all_graph_edits = []
with open(file_path, 'r') as f:
for i, line in enumerate(f):
if i % 10000 == 0:
print('Processing line {:d}'.format(i))
# Each line represents a reaction and the corresponding graph edits
#
# reaction example:
# [CH3:14][OH:15].[NH2:12][NH2:13].[OH2:11].[n:1]1[n:2][cH:3][c:4]
# ([C:7]([O:9][CH3:8])=[O:10])[cH:5][cH:6]1>>[n:1]1[n:2][cH:3][c:4]
# ([C:7](=[O:9])[NH:12][NH2:13])[cH:5][cH:6]1
# The reactants are on the left-hand-side of the reaction and the product
# is on the right-hand-side of the reaction. The numbers represent atom mapping.
#
# graph_edits example:
# 23-33-1.0;23-25-0.0
# For a triplet a-b-c, a and b are the atoms that form or loss the bond.
# c specifies the particular change, 0.0 for losing a bond, 1.0, 2.0, 3.0 and
# 1.5 separately for forming a single, double, triple or aromatic bond.
reaction, graph_edits = line.strip("\r\n ").split()
reactants = reaction.split('>')[0]
mol = Chem.MolFromSmiles(reactants)
if mol is None:
continue
# Reorder atoms according to the order specified in the atom map
atom_map_order = [-1 for _ in range(mol.GetNumAtoms())]
for i in range(mol.GetNumAtoms()):
atom = mol.GetAtomWithIdx(i)
atom_map_order[atom.GetIntProp('molAtomMapNumber') - 1] = i
mol = rdmolops.RenumberAtoms(mol, atom_map_order)
all_mols.append(mol)
all_reactions.append(reaction)
all_graph_edits.append(graph_edits)
return all_mols, all_reactions, all_graph_edits
def __len__(self):
"""Get the size for the dataset.
Returns
-------
int
Number of reactions in the dataset.
"""
return len(self.mols)
def __getitem__(self, item):
"""Get the i-th datapoint.
Returns
-------
str
Reaction.
str
Graph edits for the reaction
rdkit.Chem.rdchem.Mol
RDKit molecule instance for reactants
DGLGraph
DGLGraph for the ith molecular graph of reactants
DGLGraph
Complete DGLGraph for reactants, which will be needed for predicting
scores between each pair of atoms
float32 tensor of shape (V^2, 10)
Features for each pair of atoms.
float32 tensor of shape (V^2, 5)
Labels for reaction center prediction.
V for the number of atoms in the reactants.
"""
mol = self.mols[item]
num_atoms = mol.GetNumAtoms()
if num_atoms not in self.complete_graphs:
self.complete_graphs[num_atoms] = mol_to_complete_graph(
mol, add_self_loop=True, canonical_atom_order=True)
if self.atom_pair_features[item] is None:
reactants = self.reactions[item].split('>')[0]
self.atom_pair_features[item] = self._atom_pair_featurizer(reactants)
if self.atom_pair_labels[item] is None:
self.atom_pair_labels[item] = get_pair_label(mol, self.graph_edits[item])
return self.reactions[item], self.graph_edits[item], mol, \
self.reactant_mol_graphs[item], \
self.complete_graphs[num_atoms], \
self.atom_pair_features[item], \
self.atom_pair_labels[item]
class USPTO(WLNReactionDataset):
"""USPTO dataset for reaction prediction.
The dataset contains reactions from patents granted by United States Patent
and Trademark Office (USPTO), collected by Lowe [1]. Jin et al. removes duplicates
and erroneous reactions, obtaining a set of 480K reactions. They divide it
into 400K, 40K, and 40K for training, validation and test.
References:
* [1] Patent reaction extraction
* [2] Predicting Organic Reaction Outcomes with Weisfeiler-Lehman Network
Parameters
----------
subset : str
Whether to use the training/validation/test set as in Jin et al.
* 'train' for the training set
* 'val' for the validation set
* 'test' for the test set
mol_to_graph: callable, str -> DGLGraph
A function turning RDKit molecule instances into DGLGraphs.
Default to :func:`dgllife.utils.mol_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. By default, we consider descriptors including atom type,
atom degree, atom explicit valence, atom implicit valence, aromaticity.
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 consider descriptors including bond type,
whether bond is conjugated and whether bond is in ring.
atom_pair_featurizer : callable, str -> dict
Featurization for each pair of atoms in multiple reactants. The result will be
used to update edata in the complete DGLGraphs. By default, the features include
the bond type between the atoms (if any) and whether they belong to the same molecule.
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,
subset,
mol_to_graph=mol_to_bigraph,
node_featurizer=default_node_featurizer,
edge_featurizer=default_edge_featurizer,
atom_pair_featurizer=default_atom_pair_featurizer,
load=True):
assert subset in ['train', 'val', 'test'], \
'Expect subset to be "train" or "val" or "test", got {}'.format(subset)
print('Preparing {} subset of USPTO'.format(subset))
self._subset = subset
if subset == 'val':
subset = 'valid'
self._url = 'dataset/uspto.zip'
data_path = get_download_dir() + '/uspto.zip'
extracted_data_path = get_download_dir() + '/uspto'
download(_get_dgl_url(self._url), path=data_path)
extract_archive(data_path, extracted_data_path)
super(USPTO, self).__init__(
raw_file_path=extracted_data_path + '/{}.txt'.format(subset),
mol_graph_path=extracted_data_path + '/{}_mol_graphs.bin'.format(subset),
mol_to_graph=mol_to_graph,
node_featurizer=node_featurizer,
edge_featurizer=edge_featurizer,
atom_pair_featurizer=atom_pair_featurizer,
load=load)
@property
def subset(self):
"""Get the subset used for USPTO
Returns
-------
str
* 'train' for the training set
* 'val' for the validation set
* 'test' for the test set
"""
return self._subset
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