MDRIpred: Predicting inhibitor against drug tolrent M. Tuberculosis

MDRIPred: Predicting Inhibitors Against Drug-Tolerant Mycobacterium tuberculosis Welcome to the official repository for MDRIPred, a computational platform developed for predicting inhibitors against drug-tolerant Mycobacterium tuberculosis (H37Rv). The system was designed to identify compounds effective against both replicative and non-replicative phases of tuberculosis under carbon starvation conditions. This platform supports tuberculosis drug discovery by integrating machine learning, molecular fingerprints, pharmacophore analysis, and structural feature analysis. Web Server: http://crdd.osdd.net/oscadd/mdri/ Citation Singla, D., Tewari, R., Kumar, A., & Raghava, G. P. S. (2013).Designing of inhibitors against drug tolerant Mycobacterium tuberculosis (H37Rv).Chemistry Central Journal, 7, 49.https://doi.org/10.1186/1752-153X-7-49 https://github.com/Piyushh1104/MDRIpred.git About the Study Tuberculosis (TB), caused by Mycobacterium tuberculosis (M.tb), remains one of the deadliest infectious diseases worldwide. The emergence of multidrug-resistant (MDR) and extensively drug-resistant (XDR) strains has created an urgent need for discovering new anti-tubercular compounds. This study focuses on identifying inhibitors effective against: Replicative phase M.tb Non-replicative (drug-tolerant) phase M.tb The computational models were developed using high-throughput screening datasets and machine learning approaches to accelerate anti-tuberculosis drug discovery.