Prediction of QcrB Inhibition as a Measure of Antitubercular Activity with Machine Learning Protocols.

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 eig

SUBMITTER: Khan AA 

PROVIDER: S-EPMC9161412 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature