Discovery of Chemically Distinct DprE1 Inhibitors With Sub‐Nanomolar Predicted Potency Via Virtual Screening and Molecular Dynamics

Abstract

ABSTRACT The global spread of multidrug‐resistant tuberculosis (MDR‐TB) necessitates the discovery of novel chemotypes with strong inhibitory activity against essential bacterial enzymes. Decaprenylphosphoryl‐β‐D‐ribose 2′‐epimerase (DprE1), indispensable for cell wall biosynthesis in Mycobacterium tuberculosis , represents a validated but underexploited target. In this work, we employed an integrated computational pipeline combining pharmacophore modeling, validated docking, large‐scale virtual screening of ∼538,000 compounds, molecular dynamics simulations, MM‐PBSA free energy calculations, and machine learning–based antimicrobial prediction. This approach identified 18 previously unreported scaffolds, three of which demonstrated highly stable interactions with key catalytic residues (CYS387, LYS418, ILE131) during 200 ns molecular dynamics simulations. Binding free energy analysis confirmed favorable energetics, with Hit 1 (−28.5 kcal/mol) and Hit 3 (−24.3 kcal/mol) outperforming the reference inhibitor G1T. Machine learning predictions further suggested sub‐nanomolar MIC values for these leads, highlighting their promising anti‐tubercular potential. Scaffold novelty analysis indicated that the top hits are chemically distinct from known inhibitors. Collectively, this study reports novel and biologically relevant DprE1 inhibitors that merit further optimization and experimental validation as next‐generation anti‐TB therapeutics.