No abstract

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.

No abstract

Mycobacterium tuberculosis has been a challenging target with respect to developing interventional therapies. Over the years, several attempts at developing anti-tubercular agents have hit a dead-end due to the propensity of the tubercular bacilli to mutate rapidly. Recently, cytochrome bcc complex (QcrB) has shown some promise as a novel target of 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 large dataset 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 optimised to classify molecules accurately. Of the 24 models generated, the best performing model was one built with support vector machine and based on the ECFP6 feature set. A further screening of the known imidazopyridine amide inhibitors demonstrated that the model correctly classified the most potent molecules as actives. Thus validating the model for future applications.