Abstract
Abstract Background The rapid emergence of multidrug-resistant Mycobacterium tuberculosis (MDR-TB) has significantly reduced the effectiveness of conventional therapeutic regimens necessitating the discovery of novel drug targets and inhibitors. Main Body Recent bioinformatics-driven approaches for identifying putative inhibitors targeting essential mycobacterial proteins are comprehensively reviewed. Unlike previous reviews that tend to focus either on drug resistance mechanisms or drug discovery using computational approaches separately, this review integrates both aspects by linking genetic mutations associated with drug resistance and advanced computational approaches for anti-TB drug discovery. Integrative computational strategies including subtractive genomics, molecular docking, molecular dynamics simulations and machine learning-based prioritisation are emphasised. These approaches enable the identification of pathogen specific targets with minimal homology with the host proteins. This review further highlights the importance of natural products, peptides and drug repurposing strategies in targeting MDR-TB. Computational pipelines have shown the potential to greatly speed up early-stage drug discovery while lowering related costs and time, despite the challenges. The benefits of combining multi-omics data with artificial intelligence to create strain-specific treatment approaches are further demonstrated by case-based insights. Furthermore, this review highlights the current challenges in translating computational predictions into experimental and clinical validation while providing future directions including AI/ML based drug discovery, network pharmacology, host-directed therapies, and personalized medicine. Conclusion Overall, this review underscores the critical role of bioinformatics in addressing the global burden of MDR-TB and highlights its transformative role in guiding next-generation anti-TB drug development. Clinical trial number Not applicable.