Medicinal Chemistry: Drug Design and Development

Abstract

Medicinal chemistry has moved rapidly from treating computational drug design, ADMET prediction, and precision medicine as separate strands to integrating them within a single discovery pipeline, yet most existing reviews still address these developments in isolation. This review addresses that gap by tracing the complete drug development trajectory, from target identification through lead discovery, preclinical evaluation, and clinical translation, with attention to the technologies reshaping each stage. The historical evolution of the field is traced from early empirical discoveries to structure-based, ligand-based, and AI-integrated approaches. Molecular docking, QSAR modelling, graph neural networks, and generative molecular design are discussed as tools now used to prioritise candidates, alongside the persistent challenge of ADMET optimisation, which remains responsible for a substantial share of clinical attrition. Newer modalities, including PROTACs and antibody-drug conjugates, are considered alongside precision medicine, in which genomic and multi-omic data increasingly inform target selection and dosing. Artificial intelligence and digital twins, gene-editing-based therapeutics, green medicinal chemistry, nanomedicine, and quantum computing in molecular simulation are integrated into a single forward-looking account rather than treated as isolated emerging topics. Despite this methodological progress, fewer than 11% of candidates entering clinical trials receive regulatory approval, indicating that computational sophistication alone has not resolved the field's central translational challenge. This review provides students and early-career researchers with a grounded, integrated overview of the current state of medicinal chemistry and the obstacles that continue to warrant investigation.