Structure-Based Virtual Screening in Tuberculosis Drug Discovery Pharmacological Constraints Failure Modes and Translational Lessons
Subham Kumar Vishwakarma, Cesar Augusto Roque Borda, Oswaldo Julio Ramirez Delgado, Aditya Mishra, Zidane Qriouet, Achal Mishra, Andréía Bagliotti Meneguin, Fernando Rogerio Pavan · Future Pharmacology · 2026
Structure-based strategies are widely used in tuberculosis drug discovery; however, their translational impact remains limited. This review examines how structure-based virtual screening (SBVS) is applied in practice to Mycobacterium tuberculosis targets and explores why docking-derived predictions frequently fail to translate into measurable biological activity. Rather than treating docking scores as quantitative predictors of potency, representative case studies are analyzed to demonstrate that SBVS is most effective when employed as a prioritization framework integrated with appropriate target preparation, physicochemical filtering, and early experimental validation. Across diverse targets, molecular dynamics simulations emerge as a critical discriminator, enabling the identification of binding instability and false-positive hits that persist after static docking. Tuberculosis-specific constraints—including cofactor-dependent catalysis, resistance-associated mutations, membrane-rich environments, and permeability barriers—are discussed as key factors decoupling in silico affinity from whole-cell efficacy. Collectively, these observations support a workflow-oriented view of computational drug discovery in tuberculosis, in which iterative integration of structural modeling and experimental validation is required for meaningful lead identification.