In Silico Approaches in Benzimidazole Derivatives Research: Recent Insights

Benzimidazole remains a privileged heteroaromatic scaffold with broad therapeutic potential, spanning antimicrobial, anticancer, antitubercular, and antiviral domains. In recent years (2020-2025), computational methodologies have significantly accelerated benzimidazole-based drug discovery by elucidating structural determinants of activity and streamlining lead optimization. Molecular docking and dynamics simulations consistently reveal the scaffold's ability to engage in π-π stacking, hydrogen bonding, and hydrophobic interactions within protein active sites. Substituent modifications at C2, C5, and C6 critically modulate affinity and selectivity across diverse targets, including InhA, DprE1, kinases, and viral proteases. Complementary strategies such as QSAR, pharmacophore modeling, and in silico ADMET predictions strengthen early hit prioritization and reduce experimental attrition. Emerging approaches integrating artificial intelligence, machine learning, and free energy perturbation further enhance predictive accuracy and enable multi-target drug design. This short communication highlights recent computational insights, best practices, and future trends in benzimidazole research, emphasizing the value of combining docking, MD, QSAR, ADMET, and AI/ML workflows. Together, these advances provide a robust, cost-effective pipeline for the rational design of next-generation benzimidazole derivatives with improved efficacy and translational potential.