drug-smile-fet 1.0.1

This tool provides methods to extract meaningful features from drug SMILES for Machine Learning operation

Pre-requisites

Install RdKit library:

• instllation through anaconda
• instllation through pip

Usage

• Make sure you have Python installed in your system.
• Run Following command in the CMD.

 pip install drug-smile-fet

Example

# example.py
from dsfet import fe_1mol
import pandas as pd
train_smiles = {'DRUG_NAME': {0: 'Luminespib', 1: 'Trametinib', 2: 'Venetoclax', 3: 'Olaparib', 4: 'Axitinib'},
               'PUBCHEM_ID': {0: 135539077.0, 1: 11707110.0, 2: 49846579.0, 3: 23725625.0, 4: 6450551.0},
               'SMILES': {0: 'CCNC(=O)C1=NOC(=C1C2=CC=C(C=C2)CN3CCOCC3)C4=CC(=C(C=C4O)O)C(C)C',
                          1: 'CC1=C2C(=C(N(C1=O)C)NC3=C(C=C(C=C3)I)F)C(=O)N(C(=O)N2C4=CC=CC(=C4)NC(=O)C)C5CC5',
                          2: 'CC1(CCC(=C(C1)C2=CC=C(C=C2)Cl)CN3CCN(CC3)C4=CC(=C(C=C4)C(=O)NS(=O)(=O)C5=CC(=C(C=C5)NCC6CCOCC6)[N+](=O)[O-])OC7=CN=C8C(=C7)C=CN8)C',
                          3: 'C1CC1C(=O)N2CCN(CC2)C(=O)C3=C(C=CC(=C3)CC4=NNC(=O)C5=CC=CC=C54)F',
                          4: 'CNC(=O)C1=CC=CC=C1SC2=CC3=C(C=C2)C(=NN3)/C=C/C4=CC=CC=N4'}
               }
train_smiles_df = pd.DataFrame(data=train_smiles)

test_smile = train_smiles
test_smile_df = pd.DataFrame(test_smile)

#Train, Test, feature_sequences, feature_to_token_map = oneMolFeatureExtraction(trainSMILES=train_smiles_df, testSMILES=train_smiles_df,ngram_list=[1,2,3,4,5,6,7,8])
Train, Test, feature_sequences, feature_to_token_map = fe_1mol.oneMolFeatureExtraction(trainSMILES=train_smiles_df, testSMILES=None,ngram_list=[1,2,3,4,5,6,7,8])

Note:

The input to the method oneMolFeatureExtraction() must be a pandas DataFrame with atleats two columns:

• DRUG_NAME
• SMILES

The column name should be in capital letters.