AI Pipeline Identifies Promising Tuberculosis Drug Targets
Researchers have developed an AI-based screening pipeline that integrates machine learning and physics-based simulations to identify inhibitors of the mycobacterial toxin MenT3, a new target in tuberculosis (TB). Published in Nature, the study filtered over 100,000 compounds using ADMET-AI, PharmacoNet, docking, and molecular dynamics simulations, identifying five promising candidates for experimental validation. The MenT3 toxin is known to inhibit protein synthesis, promoting bacterial persistence. The study's hybrid computational approach aims to address the rising drug resistance in TB by discovering novel therapeutic targets. 
