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

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.

Design, synthesis and SAR of antitubercular benzylpiperazine ureas.

N-furfuryl piperazine ureas disclosed by scientists at GSK Tres Cantos were chosen as antimycobacterial hits from a phenotypic whole-cell screen. Bioisosteric replacement of the furan ring in the GSK Tres Cantos molecules with a phenyl ring led to molecule (I) with an MIC of 1 µM against Mtb H37Rv, low cellular toxicity (HepG2 IC50 ~ 80 µM), good DMPK properties and specificity for Mtb. With the aim of delineating the SAR associated with (I), fifty-five analogs were synthesized and screened against Mtb. The SAR suggests that the piperazine ring, benzyl urea and piperonyl moieties are essential signatures of this series. Active compounds in this series are metabolically stable, have low cellular toxicity and are valuable leads for optimization. Molecular docking suggests these molecules occupy the Q0 site of QcrB like Q203. Bioisosteric replacement of N-furfuryl piperazine-1-carboxamides yielded molecule (I) a novel lead with satisfactory PD, metabolism, and toxicity profiles.