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pipe.py
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pipe.py
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import pandas as pd
import numpy as np
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
dbfile = 'data/drugbank_embds.pkl'
negfile = 'data/negative_samples_thrsh_12'
fngrfile = 'data/chem_cid_SMILE_fngr_vector_fngr.pickle'
valfile = 'data/val_samples.pkl'
vfngrfile = 'data/val_fingerprints.pkl'
class Data:
def __init__(self, use_balanced_padding=True, use_fingerprint_enc=True):
self.load_maps()
self.use_balanced_padding = use_balanced_padding
self.use_fingerprint_enc = use_fingerprint_enc
def load_maps(self):
fingerprints = pd.read_pickle(fngrfile)
fingerprints['cid'] = fingerprints['cid'].astype(int)
db = pd.read_pickle(dbfile)
db = db.merge(fingerprints, how = 'right', right_on='cid', left_on='pubchem_id')
db = db[db['fingerprint'].notnull()]
db = db[(db.fingerprint.notnull()) & (db.target_amino.notnull()) & (db.target_gene.notnull())]
db['target_gene_code'] = db['target_gene'].apply(lambda x: ''.join(list(x.split('\n')[1:])))
db['target_amino_code'] = db['target_amino'].apply(lambda x: ''.join(list(x.split('\n')[1:])))
# val = pd.read_pickle('val_samples.pkl')
# self.smiles_map = pd.concat([
# drugbank[['SMILE', 'fingerprint']], val[['SMILE', 'fingerprint']]
# ], axis=0, ignore_index=True, sort=False).drop_duplicates('SMILE') \
# .rename(columns={'fingerprint', 'drug_fingerprint'})#.set_index('drug_id')
self.drug_map = db[['drug_id', 'fingerprint']].drop_duplicates('drug_id') \
.rename(columns={'fingerprint': 'drug_fingerprint'})#.set_index('drug_id')
self.target_map = db[['target_id', 'target_gene_code', 'target_amino_code']].drop_duplicates('target_id')#.set_index('gene_id')
def load_drugbank(self, target='gene'):
data = pd.read_pickle(dbfile).rename(columns={'drug_fingerprint' : 'fingerprint_em'})
data = data.merge(self.drug_map, how='left', on='drug_id').merge(self.target_map, how='left', on='target_id')
data = data[(data.drug_fingerprint.notnull()) & (data.target_amino_code.notnull()) & (data.target_gene_code.notnull())]
data = data[data.drug_fingerprint != 'nan']
return data[['drug_id', 'target_id', 'drug_fingerprint', 'target_gene_code', 'target_amino_code']]
def load_negative_samples(self):
data = pd.read_csv(negfile)[['node_0','node_1','shortest_path']]
data = data.rename(columns = {'node_0': 'drug_id', 'node_1': 'target_id'})
data = data[data['drug_id'].str.contains('DB') & data['target_id'].str.contains('BE')]
data = data.merge(self.drug_map, how='left', on='drug_id').merge(self.target_map, how='left', on='target_id')
data = data[(data.drug_fingerprint.notnull()) & (data.target_amino_code.notnull()) & (data.target_gene_code.notnull())]
data = data[data.drug_fingerprint != 'nan']
return data[['drug_id', 'target_id', 'drug_fingerprint', 'target_gene_code', 'target_amino_code']]
def encode_labels(self, l):
if l[0].count(' ') == 0:
vocab = list(set([i for j in l for i in j]))
else:
if self.use_fingerprint_enc:
return [[int(i) for i in j.replace(' ', '')] for j in l]
vocab = list(set([i for j in l for i in j.split()]))
le = LabelEncoder()
le.fit(vocab)
if l[0].count(' ') == 0:
encoding = [le.transform([i for i in j]) for j in l]
else:
encoding = [le.transform([i for i in j.split()]) for j in l]
return encoding
def pad_inputs(self, inputs):
maxlen = max(len(i) for i in inputs)
if not isinstance(inputs[0], list):
inputs = [i.tolist() for i in inputs]
if self.use_balanced_padding:
padded = [int( (maxlen - len(i)) / 2 ) * [-1] + i + int( (maxlen - len(i)) / 2 ) * [-1] for i in inputs]
padded = [i + (maxlen - len(i)) * [-1] for i in padded]
else:
padded = [i + (maxlen - len(i)) * [-1] for i in inputs]
return padded
def load_conv_train(self, thresh = 5, is_amino = True):
drugbank = self.load_drugbank()
drugbank['label'] = 1
ns = self.load_negative_samples().sample(n=len(drugbank))
ns['label'] = 0
train = pd.concat([drugbank, ns], axis=0, ignore_index=True,sort=False)
train['set'] = 'train'
if is_amino:
target_col = 'target_amino_code'
val = pd.read_pickle(valfile)
fgrprints = pd.read_pickle(vfngrfile)
val = val.merge(fgrprints, how='left', left_on='pubchem_id', right_on='cid') \
.rename(columns={'BindingDB Target Chain Sequence' : 'target_amino_code',
'fingerprint' : 'drug_fingerprint'})
val = val[(val.drug_fingerprint.notnull()) & (val.target_amino_code.notnull())]
val = val[val.drug_fingerprint != 'nan']
val.at[val[val['IC50 (nM)'].apply(np.log) < thresh].index, 'label'] = 1
val.at[val[val['IC50 (nM)'].apply(np.log) >= thresh].index, 'label'] = 0
val = val[['drug_fingerprint', 'target_amino_code', 'label']]
val['set'] = 'val'
data = pd.concat([train[['drug_fingerprint', 'target_amino_code', 'label', 'set']], val], ignore_index=True, sort=False)
else:
target_col = 'target_gene_code'
data = train[['drug_fingerprint', 'target_gene_code', 'label', 'set']].copy()
data['d_enc'] = self.encode_labels(data.drug_fingerprint.tolist())
data['t_enc'] = self.encode_labels(data[target_col].tolist())
data['d_len'] = data.d_enc.apply(len)
data['t_len'] = data.t_enc.apply(len)
data['d_enc_p'] = self.pad_inputs(data.d_enc.tolist())
data['t_enc_p'] = self.pad_inputs(data.t_enc.tolist())
return data
if __name__ == '__main__':
d = Data()
# print(d.drug_map)
# print(d.target_map)
data = d.load_conv_train(is_amino=False)
print(data.head())