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save_ligand_fps.py
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save_ligand_fps.py
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import numpy as np
import pandas as pd
import pickle
import requests, sys, json
# get pandas dataframe from filepath (activities curated)
def get_file(filepath):
def function(value):
if value<=5:
value = 0
elif value>=9:
value = 1
else:
value = value/4 - 5/4
return value
file = pd.read_csv(filepath)
file = file.astype({'pvalue':'float'})
file['pvalue'] = file['pvalue'].apply(lambda x: function(x))
file = file.drop(['target_id', 'obs'], axis=1)
file = file.drop(file[file['canonical_smiles'].isna()].index)
file = file.drop(file[file['pvalue'].isna()].index)
return file
def save_fp_values(dict_uniprot_chembl, activities_directory='F:/Jupyter_directory/TESE/activities/curated/Ki/'):
for uniprot_id in dict_uniprot_chembl:
chembl_id = dict_uniprot_chembl[uniprot_id]
# nem todos tem atividades
try:
file = get_file(activities_directory+'{}.csv'.format(chembl_id))
file = file.to_numpy()
# lista com tuplos: (fingerprint, activity_value)
tuple_list = []
for line in file:
m = Chem.MolFromSmiles(line[2])
fp = AllChem.GetMorganFingerprintAsBitVect(m,3,nBits=2048)
fp = list(fp)
tuple_fp_value = (line[0], fp, line[1])
tuple_list.append(tuple_fp_value)
pickle.dump(tuple_list, open("ki_mol_fp([fp],activity_value)/{}_with_id.pickle".format(uniprot_id), "wb"))
except Exception as e:
continue
dict_uniprot_chembl = pickle.load(open("dict_uniprot_chembl.pickle", "rb"))
save_fp_values(dict_uniprot_chembl)