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convert_chemprop_ftune.py
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import argparse
import subprocess
import warnings
import os
warnings.filterwarnings(action='ignore')
parser = argparse.ArgumentParser(description='chemprop train convert arguments')
parser.add_argument(
'--trainisc',
type=str,
default="input/train.csv",
metavar='TRAINISC',
help='the name of the train id smiles and compound file (default: input/train.csv)')
parser.add_argument(
'--name',
type=str,
default="new_family",
metavar='NAME',
help='the name of the your protein or protein family (default: new_family)')
parser.add_argument(
'--sc',
type=str,
default="kinase",
metavar='SC',
help='the name of the source checkpoint (default: kinase)')
cwd = os.getcwd()
project_file_path = "{}/TransferLearning4DTI".format(cwd.split("TransferLearning4DTI")[0])
training_files_path = "{}/TransferLearning4DTI/training_files".format(cwd.split("TransferLearning4DTI")[0])
def get_compound_ids(FileName, smiles_lst):
compound_lst = []
with open(FileName) as f:
lines = f.readlines()
for line in lines:
feature_lst = line.rstrip('\n').split(",")
if "compound" in line:
continue
if feature_lst[1] in smiles_lst:
compound_lst.append(feature_lst[0])
f.close()
return compound_lst
#get trained smiles from the csv file
def get_smiles_chemprop(FileName):
compound_lst = []
with open(FileName) as f:
lines = f.readlines()
for line in lines:
feature_lst = line.rstrip('\n').split(",")
if "smiles" in line:
continue
compound_lst.append(feature_lst[0])
f.close()
return compound_lst
def read_smiles(FileName):
smilesCompoundDict = {}
compoundSmileDict = {}
smilesClassDict = {}
c = 0
with open(FileName) as f:
lines = f.readlines()
for line in lines:
if c == 0:
c += 1
continue
id_smiles_class = line.rstrip('\n').split(",")
smilesCompoundDict[id_smiles_class[1]] = id_smiles_class[0]
compoundSmileDict[id_smiles_class[0]] = id_smiles_class[1]
smilesClassDict[id_smiles_class[1]] = id_smiles_class[2]
return smilesCompoundDict, compoundSmileDict, smilesClassDict
def read_csv_convert_to_tsv(csv_file_name, tsv_file_name, compoundSmileDict, compound_lst):
with open(tsv_file_name, 'w') as wf:
with open(csv_file_name) as f:
lines = f.readlines()
for line in lines:
if "smiles" in line:
continue
else:
csv_line = line.rstrip('\n').split(",")
comp_id = compoundSmileDict[csv_line[0]]
if comp_id in compound_lst:
wf.write(comp_id + "\t" + "\t".join([str(float(dim)) for dim in csv_line[1:]]) + "\n")
f.close()
wf.close()
def write_comp_target_features_combined_binary(new_family_path, compoundSmilesDict, smilesClassDict):
wf = open(new_family_path + "/comp_targ_binary.tsv", "w", encoding='utf-8')
for key, value in compoundSmilesDict.items():
if smilesClassDict[value] == "1":
wf.write("dummy_protein" + "\t" + key + "\t1\n")
elif smilesClassDict[value] == "0":
wf.write("dummy_protein" + "\t" + key + "\t0\n")
wf.close()
def create_folds(length):
import random
dtiList = []
for i in range(0, length):
dtiList.append(i)
random.seed(69)
random.shuffle(dtiList)
return dtiList
def write_folds(FileName, dti_list):
f = open(FileName, "w")
f.write(str(dti_list))
f.close()
def write_id_smiles(file_path, compoundSmilesDict):
wf = open(file_path, "w", encoding='utf-8')
wf.write("compound_id,smiles\n")
for key, value in compoundSmilesDict.items():
wf.write(key + "," + value + "\n")
wf.close()
def write_smiles_class(file_path, smilesClassDict):
wf = open(file_path, "w", encoding='utf-8')
wf.write("smiles,class\n")
for key, value in smilesClassDict.items():
wf.write(key + "," + value + "\n")
wf.close()
if __name__ == '__main__':
args = parser.parse_args()
train_file = args.trainisc
name = args.name
source_checkpoint = args.sc
new_family_path = "{}/{}".format(training_files_path, name)
if not os.path.exists(new_family_path):
os.makedirs(new_family_path)
smilesCompoundDict, compoundSmilesDict, smilesClassDict = read_smiles(train_file)
id_smiles_file = train_file.split(".")[0] + "_id_smiles.csv"
write_id_smiles(id_smiles_file, compoundSmilesDict)
smiles_class_file = train_file.split(".")[0] + "_smiles_class.csv"
write_smiles_class(smiles_class_file, smilesClassDict)
if not os.path.exists(project_file_path + "/output/"):
os.makedirs(project_file_path + "/output/")
train_chemprop_file = project_file_path + "/output/" + train_file.split(".")[0].split("/")[-1] + "_chemprop.csv"
print("chemprop_fingerprint is running")
cmdCommand = "chemprop_fingerprint --test_path " + smiles_class_file + " --checkpoint_path chemprop/" + source_checkpoint + "_checkpoints/model.pt " \
"--preds_path " + train_chemprop_file # specify your cmd command
# print(cmdCommand)
process = subprocess.Popen(cmdCommand.split())
output, error = process.communicate()
#create compound feature vector for the new family
if not os.path.exists(new_family_path + "/compound_feature_vectors/"):
os.makedirs(new_family_path + "/compound_feature_vectors/")
tsv_file_name = new_family_path + "/compound_feature_vectors/chemprop.tsv"
smiles_lst = get_smiles_chemprop(train_chemprop_file)
compound_lst = get_compound_ids(train_file, smiles_lst)
read_csv_convert_to_tsv(train_chemprop_file, tsv_file_name, smilesCompoundDict, compound_lst)
print("Training chemprop file is converted")
#create comp_targ_binary file for the new family
write_comp_target_features_combined_binary(new_family_path, compoundSmilesDict, smilesClassDict)
print("Compound target binary file is created")
#create train fold file for the new family
fold_list = create_folds(len(compound_lst))
if not os.path.exists(new_family_path + "/data/folds/"):
os.makedirs(new_family_path + "/data/folds/")
write_folds(new_family_path + "/data/folds/train_fold_setting1.txt", fold_list)
print("Training fold is created")