Open AccessPrediction of QcrB Inhibition as a Measure of Antitubercular Activity with Machine Learning Protocols
Author(s) -
Afreen A. Khan,
Sannidhi S. Poojary,
Ketki Bhave,
Santosh Nandan,
Kaushik A. Iyer,
Evans C. Coutinho
Publication year - 2022
Publication title -
acs omega
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.779
H-Index - 40
ISSN - 2470-1343
DOI - 10.1021/acsomega.2c01613
Subject(s) - classifier (uml) , artificial intelligence , support vector machine , computer science , mycobacterium tuberculosis , machine learning , imidazopyridine , pattern recognition (psychology) , tuberculosis , chemistry , medicine , combinatorial chemistry , pathology
It has always been a challenge to develop interventional therapies for Mycobacterium tuberculosis . Over the years, several attempts at developing such therapies have hit a dead-end owing to rapid mutation rates of the tubercular bacilli and their ability to lay dormant for years. Recently, cytochrome bcc complex (QcrB) has shown some promise as a novel target against the tubercular bacilli, with Q203 being the first molecule acting on this target. In this paper, we report the deployment of several ML-based approaches to design molecules against QcrB. Machine learning (ML) models were developed based on a data set of 350 molecules using three different sets of molecular features, i.e., MACCS keys, ECFP6 fingerprints, and Mordred descriptors. Each feature set was trained on eight ML classifier algorithms and optimized to classify molecules accurately. The support vector machine-based classifier using the ECFP6 feature set was found to be the best classifier in this study. Further, screening of the known imidazopyridine amide inhibitors demonstrated that the model correctly classified the most potent molecules as actives, hence validating the model for future applications.