Accelerating Drug Discovery: Computational and Structural Tools for Medicinal Chemistry

Drug discovery remains one of the most complex and resource-intensive endeavors in the biomedical sciences. Despite significant progress in computational methodologies, translating in silico predictions into viable drug candidates continues to pose major challenges. The expanding chemical space, the structural complexity of therapeutic targets — including protein–protein interfaces and intrinsically disordered proteins — and the persistent gap between virtual screening hits and clinically relevant leads all underscore the need for more precise and reliable tools. Recent advances in molecular modeling, structural bioinformatics, and quantitative structure–activity relationship (QSAR) analysis offer promising avenues to improve hit identification, lead optimization, and ultimately the success rate of preclinical drug development. However, realizing this potential requires stronger integration of computational rigor with medicinal chemistry expertise and experimental validation.This Research Topic aims to highlight how advanced computational and structural approaches can directly support and accelerate medicinal chemistry decision-making. We seek contributions that demonstrate the practical impact of these tools on drug design workflows — from target identification and hit discovery through lead optimization and candidate selection. Emphasis is placed on work that connects algorithmic or structural innovation to measurable improvements in potency, selectivity, pharmacokinetic properties, or developability, grounded in chemical principles and structural logic rather than automated learning alone.We welcome Original Research, Review, Mini Review and Perspective articles on themes including, but not limited to:o QSAR/QSPR modeling and molecular descriptors applied to lead optimizationo Structure-based drug design using advanced docking and molecular dynamicso Computational strategies for improving selectivity and reducing off-target effectso Integrative workflows linking in silico prediction to experimental hit-to-lead progressiono Chemoinformatics tools for navigating structure–activity and structure–property landscapeso Target-specific design for challenging systems such as protein–protein interactionso Multi-scale modeling approaches bridging molecular and pharmacological outcomeso Methods to reduce attrition rates and false positives in virtual screening campaigns Topic editors: Lorentz Jäntschi, Anna Tsantili-Kakoulidou Source: https://www.frontiersin.org/research-topics/83502/accelerating-drug-discovery-computational-and-structural-tools-for-medicinal-chemistry