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import pickle
from MoleculeACE.benchmark.cliffs import ActivityCliffs, get_tanimoto_matrix, \
moleculeace_similarity, get_fc
from sklearn.cluster import SpectralClustering
from sklearn.model_selection import train_test_split
from chemprop.data import MoleculeDataset
from typing import List
import pandas as pd
import numpy as np
import random
import torch
from tqdm import tqdm
from chemprop.data.utils import get_data, get_task_names
from utils import check_molecule, get_protein_sequence, \
get_molecule_feature, get_protein_feature, generate_onehot_features
from DeepPurpose.utils import encode_drug, encode_protein
from rdkit import Chem
from rdkit.Chem import AllChem
import networkx as nx
from torch.utils import data
from torch_geometric.data import DataLoader
from CPI_baseline.utils import TestbedDataset, MolTrans_Data_Encoder
def process_data_CPI(args, logger):
args.smiles_columns = ['smiles']
args.target_columns = ['y']
df_data = pd.DataFrame()
chembl_list = []
if args.split_sizes:
_, valid_ratio, test_ratio = args.split_sizes
if not os.path.exists(args.data_path):
# integrate bioactivity data
if 'MoleculeACE' in args.data_path:
dataset = MOLECULEACE_DATALIST
datadir = 'MoleculeACE'
elif 'Ours' in args.data_path:
dataset = OUR_DATALIST
datadir = 'Ours'
# logger.info(f'Integrating data from {datadir}...')
for assay_name in dataset:
df = pd.read_csv(f'data/{datadir}/{assay_name}.csv')
df[args.smiles_columns] = df[args.smiles_columns].applymap(check_molecule)
df = df.dropna(subset=args.smiles_columns)
if 'split' not in df.columns and 'cliff_mol' not in df.columns:
df = split_data(df[args.smiles_columns].values,
bioactivity=df[args.target_columns].values,
in_log10=True, similarity=0.9, test_size=test_ratio, random_state=args.seed)
df.to_csv(args.data_path, index=False)
df['Chembl_id'] = df['Uniprot_id']
df_data = pd.concat([df_data, df])
chembl_list.append(assay_name.split('_')[0])
args.ignore_columns = ['exp_mean [nM]', 'split', 'cliff_mol']
pos_num, neg_num = len(df_data[df_data['cliff_mol']==1]), len(df_data[df_data['cliff_mol']==0])
if args.print:
logger.info(f'ACs: {pos_num}, non-ACs: {neg_num}')
# protein ID mapping and sequence retrieval
logger.info('Mapping ChEMBL IDs to UniProt IDs...')
# chembl_uni = dict(zip(chembl_list,
# [chembl_to_uniprot(chembl_id) for chembl_id in chembl_list]))
if args.print:
logger.info('Getting target sequences...')
uni_seq = dict(zip(chembl_uni.values(),
[get_protein_sequence(uni_id) for uni_id in chembl_uni.values()]))
df_data['Uniprot_id'] = df_data['Chembl_id'].map(chembl_uni)
df_data['Sequence'] = df_data['Uniprot_id'].map(uni_seq)
df_data = df_data.dropna(subset=['Uniprot_id', 'Sequence'])
df_data = df_data.reset_index(drop=True)
if args.print:
logger.info(f'Saving data to {args.data_path}')
df_data.to_csv(args.data_path, index=False)
else:
df_data = pd.read_csv(args.data_path)
args.ignore_columns = None
if args.print:
logger.info(f'Loading data from {args.data_path}')
X_drug = df_data['smiles'].values
X_target = df_data['Sequence'].values
y = df_data['y'].values
train_idx, test_idx = list(df_data[df_data['split'].values == 'train'].index), \
list(df_data[df_data['split'].values == 'test'].index)
if args.mode in ['inference', 'baseline_inference']:
val_idx = []
logger.info(f'test size: {len(test_idx)}') if args.print else None
else:
# else:
if 'valid' in df_data['split'].values:
val_idx = list(df_data[df_data['split'].values == 'valid'].index)
else:
val_idx = random.sample(list(train_idx), int(len(train_idx) * valid_ratio))
df_data.loc[val_idx, 'split'] = 'valid'
# train_idx = list(set(train_idx) - set(val_idx))
logger.info(f'total size: {len(df_data)}, train size: {len(train_idx)}, '
f'val size: {len(val_idx)}, test size: {len(test_idx)}') if args.print else None
if args.mode in ['train', 'inference', 'retrain', 'finetune'] \
and args.train_model in ['GGAP_CPI', 'KANO_ESM']:
# get data from csv file
args.task_names = get_task_names(args.data_path, args.smiles_columns,
args.target_columns, args.ignore_columns)
if 'type_id' not in df_data.columns:
df_data['type_id'] = 1
df_data.to_csv(args.data_path, index=False)
logger.info('Adding dummy type_id column to the dataset...')
data = get_data(path=args.data_path,
smiles_columns=args.smiles_columns,
target_columns=args.target_columns,
ignore_columns=args.ignore_columns)
train_data, val_data, test_data = tuple([[data[i] for i in train_idx],
[data[i] for i in val_idx] if len(val_idx) > 0 else [],
[data[i] for i in test_idx]])
train_data, val_data, test_data = MoleculeDataset(train_data), \
MoleculeDataset(val_data), \
MoleculeDataset(test_data)
elif args.mode in ['baseline_CPI', 'baseline_inference'] and args.baseline_model == 'DeepDTA':
df = pd.DataFrame(zip(X_drug, X_target, y))
df.rename(columns={0:'SMILES', 1: 'Sequence', 2: 'Label'}, inplace=True)
drug_encoding = 'CNN'
target_encoding = 'CNN'
df = encode_drug(df, drug_encoding, 'SMILES', 'drug_encoding')
df = encode_protein(df, target_encoding, 'Sequence', 'target_encoding')
train_data, val_data, test_data = df.iloc[train_idx], df.iloc[val_idx], df.iloc[test_idx]
train_data = train_data.reset_index(drop=True)
val_data = val_data.reset_index(drop=True)
test_data = test_data.reset_index(drop=True)
elif args.mode in ['baseline_CPI', 'baseline_inference'] and args.baseline_model == 'HyperAttentionDTI':
from torch.utils.data import DataLoader
from CPI_baseline.HyperAttentionDTI import hyperparameter
from CPI_baseline.utils import collate_fn
train_data = df_data.iloc[train_idx].reset_index(drop=True)
train_data['Drug'] = train_data.index
val_data = df_data.iloc[val_idx].reset_index(drop=True)
val_data['Drug'] = val_data.index
test_data = df_data.iloc[test_idx].reset_index(drop=True)
test_data['Drug'] = test_data.index
train_data = train_data['Drug'].astype(str) + ' ' \
+ train_data['Uniprot_id'].astype(str) + ' ' \
+ train_data['smiles'].astype(str) + ' ' \
+ train_data['Sequence'].astype(str) + ' ' \
+ train_data['y'].astype(str)
train_data = train_data.values.tolist()
val_data = val_data['Drug'].astype(str) + ' ' \
+ val_data['Uniprot_id'].astype(str) + ' ' \
+ val_data['smiles'].astype(str) + ' ' \
+ val_data['Sequence'].astype(str) + ' ' \
+ val_data['y'].astype(str)
val_data = val_data.values.tolist()
test_data = test_data['Drug'].astype(str) + ' ' \
+ test_data['Uniprot_id'].astype(str) + ' ' \
+ test_data['smiles'].astype(str) + ' ' \
+ test_data['Sequence'].astype(str) + ' ' \
+ test_data['y'].astype(str)
test_data = test_data.values.tolist()
hp = hyperparameter()
train_data = DataLoader(train_data, batch_size=hp.Batch_size, shuffle=True, collate_fn=collate_fn) \
if len(train_data) > 0 else []
val_data = DataLoader(val_data, batch_size=hp.Batch_size, shuffle=False, collate_fn=collate_fn) \
if len(val_data) > 0 else []
test_data = DataLoader(test_data, batch_size=hp.Batch_size, shuffle=False, collate_fn=collate_fn) \
if len(test_data) > 0 else []
elif args.mode in ['baseline_CPI', 'baseline_inference'] and args.baseline_model == 'GraphDTA':
from torch_geometric.data import DataLoader
train_data = df_data.iloc[train_idx].reset_index(drop=True)
val_data = df_data.iloc[val_idx].reset_index(drop=True)
test_data = df_data.iloc[test_idx].reset_index(drop=True)
train_graph = {}
if args.print:
logger.info('Training set: converting SMILES to graph data...')
for s in tqdm(train_data['smiles'].values):
g = smiles_to_graph(s)
train_graph[s] = g
val_graph = {}
if args.print:
logger.info('Validation set: converting SMILES to graph data...')
for s in tqdm(val_data['smiles'].values):
g = smiles_to_graph(s)
val_graph[s] = g
test_graph = {}
if args.print:
logger.info('Test set: converting SMILES to graph data...')
for s in tqdm(test_data['smiles'].values):
g = smiles_to_graph(s)
test_graph[s] = g
train_smiles, val_smiles, test_smiles = train_data['smiles'].values, \
val_data['smiles'].values, \
test_data['smiles'].values
train_protein = [seq_cat(t) for t in train_data['Sequence'].values]
val_protein = [seq_cat(t) for t in val_data['Sequence'].values]
test_protein = [seq_cat(t) for t in test_data['Sequence'].values]
train_label, val_label, test_label = train_data['y'].values, \
val_data['y'].values, \
test_data['y'].values
if len (train_data) > 0:
train_data = TestbedDataset(root=args.save_path, dataset=args.data_name+'_train',
xd=train_smiles, xt=train_protein, y=train_label, smile_graph=train_graph)
train_data = DataLoader(train_data, batch_size=512, shuffle=True)
else:
train_data = []
if len(val_data) > 0:
val_data = TestbedDataset(root=args.save_path, dataset=args.data_name+'_val',
xd=val_smiles, xt=val_protein, y=val_label, smile_graph=val_graph)
val_data = DataLoader(val_data, batch_size=512, shuffle=False)
else:
val_data = []
if len(test_data) > 0:
print(len(test_data))
test_data = TestbedDataset(root=args.save_path, dataset=args.data_name+'_test',
xd=test_smiles, xt=test_protein, y=test_label, smile_graph=test_graph)
# error_smi = test_data.error_smi
test_data = DataLoader(test_data, batch_size=512, shuffle=False)
# df_data = df_data[~df_data['smiles'].isin(error_smi)]
else:
test_data = []
elif args.mode in ['baseline_CPI', 'baseline_inference'] and args.baseline_model == 'MolTrans':
from torch.utils import data
train_data = df_data.iloc[train_idx].reset_index(drop=True)
val_data = df_data.iloc[val_idx].reset_index(drop=True)
test_data = df_data.iloc[test_idx].reset_index(drop=True)
train_data = MolTrans_Data_Encoder(train_data.index.values,
train_data['y'].values, train_data)
train_data = data.DataLoader(train_data, batch_size=64, shuffle=True, drop_last=True)
if len(val_data) > 0:
val_data = MolTrans_Data_Encoder(val_data.index.values,
val_data['y'].values, val_data)
val_data = data.DataLoader(val_data, batch_size=64, shuffle=False, drop_last=True)
else:
val_data = []
test_data = MolTrans_Data_Encoder(test_data.index.values,
test_data['y'].values, test_data)
test_data = data.DataLoader(test_data, batch_size=64, shuffle=False, drop_last=False)
elif args.mode in ['baseline_CPI', 'baseline_inference'] and args.baseline_model in ['ECFP_ESM_GBM', 'ECFP_ESM_RF']:
train_data = df_data.iloc[train_idx].reset_index(drop=True)
val_data = df_data.iloc[val_idx].reset_index(drop=True)
test_data = df_data.iloc[test_idx].reset_index(drop=True)
train_data = train_data[['smiles', 'Uniprot_id', 'y', 'Sequence']]
val_data = val_data[['smiles', 'Uniprot_id', 'y', 'Sequence']]
test_data = test_data[['smiles', 'Uniprot_id', 'y', 'Sequence']]
# calculate ECFP4 fingerprints
train_mol, val_mol, test_mol = [Chem.MolFromSmiles(smi) for smi in train_data['smiles'].values], \
[Chem.MolFromSmiles(smi) for smi in val_data['smiles'].values], \
[Chem.MolFromSmiles(smi) for smi in test_data['smiles'].values]
train_mol, val_mol, test_mol = [AllChem.GetMorganFingerprintAsBitVect(m, radius=2, nBits=2048) for m in train_mol], \
[AllChem.GetMorganFingerprintAsBitVect(m, radius=2, nBits=2048) for m in val_mol], \
[AllChem.GetMorganFingerprintAsBitVect(m, radius=2, nBits=2048) for m in test_mol]
prot_graph = get_protein_feature(args, logger, df_data)
train_prot = [torch.mean(prot_graph[idx].x, dim=0).cpu().numpy()
for idx in train_data['Uniprot_id'].values]
val_prot = [torch.mean(prot_graph[idx].x, dim=0).cpu().numpy()
for idx in val_data['Uniprot_id'].values]
test_prot = [torch.mean(prot_graph[idx].x, dim=0).cpu().numpy()
for idx in test_data['Uniprot_id'].values]
# concatenate ECFP4 and protein features
if len(train_data) > 0:
train_feat = np.concatenate([np.array(train_mol), np.array(train_prot)], axis=1)
train_data = [train_data['y'].values, train_feat]
else:
train_feat = []
if len(val_data) > 0:
val_feat = np.concatenate([np.array(val_mol), np.array(val_prot)], axis=1)
val_data = [val_data['y'].values, val_feat]
else:
val_feat = []
if len(test_data) > 0:
test_feat = np.concatenate([np.array(test_mol), np.array(test_prot)], axis=1)
test_data = [test_data['y'].values, test_feat]
else:
test_feat = []
elif args.mode in ['baseline_CPI', 'baseline_inference'] and args.baseline_model in ['KANO_ESM_GBM', 'KANO_ESM_RF']:
train_data = df_data.iloc[train_idx].reset_index(drop=True)
val_data = df_data.iloc[val_idx].reset_index(drop=True)
test_data = df_data.iloc[test_idx].reset_index(drop=True)
train_data = train_data[['smiles', 'Uniprot_id', 'y', 'Sequence']]
val_data = val_data[['smiles', 'Uniprot_id', 'y', 'Sequence']]
test_data = test_data[['smiles', 'Uniprot_id', 'y', 'Sequence']]
if not os.path.exists(os.path.join(args.save_path, 'train_mol.pkl' \
if args.mode != 'baseline_inference' else 'train_mol_infer.pkl')):
train_mol = get_molecule_feature(args, logger, train_data['smiles'].values)
pickle.dump(train_mol, open(os.path.join(args.save_path, 'train_mol.pkl'\
if args.mode != 'baseline_inference' else 'train_mol_infer.pkl'), 'wb'))
else:
train_mol = pickle.load(open(os.path.join(args.save_path, f'{args.data_name}_train_mol.pkl' \
if args.mode != 'baseline_inference' else f'{args.data_name}_train_mol_infer.pkl'), 'rb'))
if not os.path.exists(os.path.join(args.save_path, f'{args.data_name}_val_mol.pkl' \
if args.mode != 'baseline_inference' else f'{args.data_name}_val_mol_infer.pkl')):
val_mol = get_molecule_feature(args, logger, val_data['smiles'].values)
pickle.dump(val_mol, open(os.path.join(args.save_path, f'{args.data_name}_val_mol.pkl' \
if args.mode != 'baseline_inference' else f'{args.data_name}_val_mol_infer.pkl'), 'wb'))
else:
val_mol = pickle.load(open(os.path.join(args.save_path, f'{args.data_name}_val_mol.pkl'\
if args.mode != 'baseline_inference' else f'{args.data_name}_val_mol_infer.pkl'), 'rb'))
if not os.path.exists(os.path.join(args.save_path, f'{args.data_name}_test_mol.pkl'\
if args.mode != 'baseline_inference' else f'{args.data_name}_test_mol_infer.pkl')):
test_mol = get_molecule_feature(args, logger, test_data['smiles'].values)
pickle.dump(test_mol, open(os.path.join(args.save_path, f'{args.data_name}_test_mol.pkl'\
if args.mode != 'baseline_inference' else f'{args.data_name}_test_mol_infer.pkl'), 'wb'))
else:
test_mol = pickle.load(open(os.path.join(args.save_path, f'{args.data_name}_test_mol.pkl'\
if args.mode != 'baseline_inference' else f'{args.data_name}_test_mol_infer.pkl'), 'rb'))
prot_graph = get_protein_feature(args, logger, df_data)
train_prot = [torch.mean(prot_graph[idx].x, dim=0).cpu().numpy()
for idx in train_data['Uniprot_id'].values]
val_prot = [torch.mean(prot_graph[idx].x, dim=0).cpu().numpy()
for idx in val_data['Uniprot_id'].values]
test_prot = [torch.mean(prot_graph[idx].x, dim=0).cpu().numpy()
for idx in test_data['Uniprot_id'].values]
if len(train_data) > 0:
train_feat = np.concatenate([np.array(train_mol), np.array(train_prot)], axis=1)
train_data = [train_data['y'].values, train_feat]
else:
train_feat = []
if len(val_data) > 0:
val_feat = np.concatenate([np.array(val_mol), np.array(val_prot)], axis=1)
val_data = [val_data['y'].values, val_feat]
else:
val_feat = []
if len(test_data) > 0:
test_feat = np.concatenate([np.array(test_mol), np.array(test_prot)], axis=1)
test_data = [test_data['y'].values, test_feat]
else:
test_feat = []
return df_data, test_idx, train_data, val_data, test_data
def process_data_QSAR(args, logger):
# check the validity of SMILES
df = pd.read_csv(args.data_path)
df[args.smiles_columns] = df[args.smiles_columns].apply(check_molecule)
df = df.dropna(subset=args.smiles_columns)
df = df.reset_index(drop=True)
if args.split_sizes:
_, valid_ratio, test_ratio = args.split_sizes
# get splitting index and calculate the activity cliff based on MoleculeACE
if 'split' not in df.columns and 'cliff_mol' not in df.columns:
df = split_data(df[args.smiles_columns].values.tolist(),
bioactivity=df[args.target_columns].values.tolist(),
in_log10=True, similarity=0.9, test_size=test_ratio, random_state=args.seed)
df.to_csv(args.data_path, index=False)
args.ignore_columns = ['exp_mean [nM]', 'split', 'cliff_mol']
else:
args.ignore_columns = None
pos_num, neg_num = len(df[df['cliff_mol']==1]), len(df[df['cliff_mol']==0])
if args.print:
logger.info(f'ACs: {pos_num}, non-ACs: {neg_num}')
# get data from csv file
args.task_names = get_task_names(args.data_path, args.smiles_columns,
args.target_columns, args.ignore_columns)
data = get_data(path=args.data_path,
smiles_columns=args.smiles_columns,
target_columns=args.target_columns,
ignore_columns=args.ignore_columns)
# split data by MoleculeACE
if args.split_sizes:
train_idx, test_idx = df[df['split']=='train'].index, df[df['split']=='test'].index
if 'valid' in df['split'].values:
val_idx = df[df['split']=='valid'].index
else:
val_idx = random.sample(list(train_idx), int(len(train_idx) * valid_ratio))
train_idx = list(set(train_idx) - set(val_idx))
train_data, val_data, test_data = tuple([[data[i] for i in train_idx],
[data[i] for i in val_idx],
[data[i] for i in test_idx]])
train_data, val_data, test_data = MoleculeDataset(train_data), \
MoleculeDataset(val_data), \
MoleculeDataset(test_data)
if args.print:
logger.info(f'total size: {len(data)}, train size: {len(train_data)}, '
f'val size: {len(val_data)}, test size: {len(test_data)}')
return df, test_idx, train_data, val_data, test_data
def split_data(smiles: List[str], bioactivity: List[float], n_clusters: int = 5,
in_log10 = True, test_size: float = 0.2, random_state: int = 0,
similarity: float = 0.9, potency_fold: int = 10, remove_stereo: bool = True):
""" Split data into train/test according to activity cliffs and compounds characteristics.
:param smiles: (List[str]) list of SMILES strings
:param bioactivity: (List[float]) list of bioactivity values
:param n_clusters: (int) number of clusters the data is split into for getting homogeneous data splits
:param in_log10: (bool) are the bioactivity values in log10?
:param test_size: (float) test split
:param similarity: (float) similarity threshold for calculating activity cliffs
:param potency_fold: (float) potency difference threshold for calculating activity cliffs
:param remove_stereo: (bool) Remove racemic mixtures altogether?
:return: df[smiles, exp_mean [nM], y, cliff_mol, split]
"""
original_smiles = smiles
original_bioactivity = bioactivity
if remove_stereo:
stereo_smiles_idx = [smiles.index(i) for i in find_stereochemical_siblings(smiles)]
smiles = [smi for i, smi in enumerate(smiles) if i not in stereo_smiles_idx]
bioactivity = [act for i, act in enumerate(bioactivity) if i not in stereo_smiles_idx]
if len(stereo_smiles_idx) > 0:
print(f"Removed {len(stereo_smiles_idx)} stereoisomers")
check_matching(original_smiles, original_bioactivity, smiles, bioactivity)
if not in_log10:
y_log = -np.log10(bioactivity)
else:
y_log = bioactivity
cliffs = ActivityCliffs(smiles, bioactivity)
cliff_mols = cliffs.get_cliff_molecules(return_smiles=False, similarity=similarity, potency_fold=potency_fold)
check_cliffs(cliffs)
# Perform spectral clustering on a tanimoto distance matrix
spectral = SpectralClustering(n_clusters=n_clusters, random_state=random_state, affinity='precomputed')
clusters = spectral.fit(get_tanimoto_matrix(smiles)).labels_
train_idx, test_idx = [], []
for cluster in range(n_clusters):
cluster_idx = np.where(clusters == cluster)[0]
clust_cliff_mols = [cliff_mols[i] for i in cluster_idx]
# Can only split stratiefied on cliffs if there are at least 2 cliffs present, else do it randomly
if sum(clust_cliff_mols) > 2:
clust_train_idx, clust_test_idx = train_test_split(cluster_idx, test_size=test_size,
random_state=random_state,
stratify=clust_cliff_mols, shuffle=True)
else:
clust_train_idx, clust_test_idx = train_test_split(cluster_idx, test_size=test_size,
random_state=random_state,
shuffle=True)
train_idx.extend(clust_train_idx)
test_idx.extend(clust_test_idx)
train_test = []
for i in range(len(smiles)):
if i in train_idx:
train_test.append('train')
elif i in test_idx:
train_test.append('test')
else:
raise ValueError(f"Can't find molecule {i} in train or test")
# Check if there is any intersection between train and test molecules
assert len(np.intersect1d(train_idx, test_idx)) == 0, 'train and test intersect'
assert len(np.intersect1d(np.array(smiles)[np.where(np.array(train_test) == 'train')],
np.array(smiles)[np.where(np.array(train_test) == 'test')])) == 0, \
'train and test intersect'
df_out = pd.DataFrame({'smiles': smiles,
'exp_mean [nM]': bioactivity,
'y': y_log,
'cliff_mol': cliff_mols,
'split': train_test})
return df_out
def process_data(smiles: List[str], bioactivity: List[float], n_clusters: int = 5, test_size: float = 0.2,
similarity: float = 0.9, potency_fold: int = 10, remove_stereo: bool = False):
""" Split data into train/test according to activity cliffs and compounds characteristics.
:param smiles: (List[str]) list of SMILES strings
:param bioactivity: (List[float]) list of bioactivity values
:param n_clusters: (int) number of clusters the data is split into for getting homogeneous data splits
:param test_size: (float) test split
:param similarity: (float) similarity threshold for calculating activity cliffs
:param potency_fold: (float) potency difference threshold for calculating activity cliffs
:param remove_stereo: (bool) Remove racemic mixtures altogether?
:return: df[smiles, exp_mean [nM], y, cliff_mol, split]
"""
return split_data(smiles, bioactivity, n_clusters, test_size, similarity, potency_fold, remove_stereo)
def fetch_data(chembl_targetid='CHEMBL2047', endpoints=['EC50']):
"""Download and prep the data from CHEMBL. Throws out duplicates, problematic molecules, and extreme outliers"""
from MoleculeACE.benchmark.data_fetching import main_curator
import os
# fetch + curate data
data = main_curator.main(chembl_targetid=chembl_targetid, endpoints=endpoints)
# write to Data directory
filename = os.path.join('Data', f"{chembl_targetid}_{'_'.join(endpoints)}.csv")
data.to_csv(filename)
def find_stereochemical_siblings(smiles: List[str]):
""" Detects molecules that have different SMILES strings, but ecode for the same molecule with
different stereochemistry. For racemic mixtures it is often unclear which one is measured/active
Args:
smiles: (lst) list of SMILES strings
Returns: (lst) List of SMILES having a similar molecule with different stereochemistry
"""
from MoleculeACE.benchmark.cliffs import get_tanimoto_matrix
lower = np.tril(get_tanimoto_matrix(smiles, radius=4, nBits=4096), k=0)
identical = np.where(lower == 1)
identical_pairs = [[smiles[identical[0][i]], smiles[identical[1][i]]] for i, j in enumerate(identical[0])]
return list(set(sum(identical_pairs, [])))
def check_matching(original_smiles, original_bioactivity, smiles, bioactivity):
assert len(smiles) == len(bioactivity), "length doesn't match"
for smi, label in zip(original_smiles, original_bioactivity):
if smi in smiles:
assert bioactivity[smiles.index(smi)] == label, f"{smi} doesn't match label {label}"
def is_cliff(smiles1, smiles2, y1, y2, similarity: float = 0.9, potency_fold: float = 10):
""" Calculates if two molecules are activity cliffs """
sim = moleculeace_similarity([smiles1, smiles2], similarity=similarity)[0][1]
fc = get_fc([y1, y2])[0][1]
return sim == 1 and fc >= potency_fold
def check_cliffs(cliffs, n: int = 10):
# Find the location of 10 random cliffs and check if they are actually cliffs
m = n
if np.sum(cliffs.cliffs) < 2*n:
n = int(np.sum(cliffs.cliffs)/2)
cliff_loc = np.where(cliffs.cliffs == 1)
random_cliffs = np.random.randint(0, len(cliff_loc[0]), n)
cliff_loc = [(cliff_loc[0][c], cliff_loc[1][c]) for c in random_cliffs]
for i, j in cliff_loc:
assert is_cliff(cliffs.smiles[i], cliffs.smiles[j], cliffs.bioactivity[i], cliffs.bioactivity[j])
if len(cliffs.cliffs)-n < m:
m = len(cliffs.cliffs)-n
# Find the location of 10 random non-cliffs and check if they are actually non-cliffs
non_cliff_loc = np.where(cliffs.cliffs == 0)
random_non_cliffs = np.random.randint(0, len(non_cliff_loc[0]), m)
non_cliff_loc = [(non_cliff_loc[0][c], non_cliff_loc[1][c]) for c in random_non_cliffs]
for i, j in non_cliff_loc:
assert not is_cliff(cliffs.smiles[i], cliffs.smiles[j], cliffs.bioactivity[i], cliffs.bioactivity[j])
# Convertion from SMILES to graph data for GraphDTA
def atom_features(atom):
return np.array(one_of_k_encoding_unk(atom.GetSymbol(),['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', 'Unknown']) +
one_of_k_encoding(atom.GetDegree(), [0, 1, 2, 3, 4, 5, 6,7,8,9,10]) +
one_of_k_encoding_unk(atom.GetTotalNumHs(), [0, 1, 2, 3, 4, 5, 6,7,8,9,10]) +
one_of_k_encoding_unk(atom.GetImplicitValence(), [0, 1, 2, 3, 4, 5, 6,7,8,9,10]) +
[atom.GetIsAromatic()])
def one_of_k_encoding(x, allowable_set):
if x not in allowable_set:
raise Exception("input {0} not in allowable set{1}:".format(x, allowable_set))
return list(map(lambda s: x == s, allowable_set))
def one_of_k_encoding_unk(x, allowable_set):
"""Maps inputs not in the allowable set to the last element."""
if x not in allowable_set:
x = allowable_set[-1]
return list(map(lambda s: x == s, allowable_set))
def smiles_to_graph(smile):
mol = Chem.MolFromSmiles(smile)
c_size = mol.GetNumAtoms()
features = []
for atom in mol.GetAtoms():
feature = atom_features(atom)
features.append( feature / sum(feature) )
edges = []
for bond in mol.GetBonds():
edges.append([bond.GetBeginAtomIdx(), bond.GetEndAtomIdx()])
g = nx.Graph(edges).to_directed()
edge_index = []
for e1, e2 in g.edges:
edge_index.append([e1, e2])
return c_size, features, edge_index
def seq_cat(prot, max_seq_len=1000):
seq_voc = "ABCDEFGHIKLMNOPQRSTUVWXYZ"
seq_dict = {v:(i+1) for i,v in enumerate(seq_voc)}
x = np.zeros(max_seq_len)
for i, ch in enumerate(prot[:max_seq_len]):
x[i] = seq_dict[ch]
return x |