Abstract
Computational modeling has gained remarkable prominence in medicinal chemistry, offering a powerful framework for accelerating the discovery and optimization of triazole-based therapeutics. These derivatives, widely recognized for their antifungal, anticancer, and antiviral activities, have been extensively studied through the combined application of Quantitative Structure Activity Relationship (QSAR) modeling and molecular docking. While QSAR analysis quantitatively links molecular descriptors to biological activity, docking simulations reveal binding orientations, interaction profiles, and affinity patterns. Recent advances incorporating machine learning and artificial intelligence have markedly improved predictive performance, enabling more rational lead selection. Nonetheless, current limitations such as incomplete or low-quality datasets, reduced interpretability, and biological oversimplifications pose significant challenges. To address these, hybrid approaches that integrate physics-based modeling with data-driven algorithms, together with innovations such as ensemble docking, explainable AI, and quantum mechanics-based simulations, are emerging as promising solutions. By leveraging these developments, computational strategies can more effectively bridge the translational gap between in silico predictions and experimental validation, ultimately reducing attrition rates and expediting the drug development pipeline.
Keywords
Drug discovery; Triazole derivatives; QSAR modeling;AI-enhanced modeling; Molecular docking