FOUNDATIONS OF MOLECULAR DRUG DESIGN: LEAD IDENTIFICATION AND OPTIMIZATION

Le résumé fourni par la source

Molecular drug design has transformed the early stages of drug discovery by integrating structural biology, computational chemistry, and high-throughput analytical approaches to accelerate target identification and lead optimization. Advances in structure-based drug design (SBDD), ligand-based drug design (LBDD), pharmacophore modeling, virtual screening, and molecular docking have significantly reduced discovery timelines and costs, with in silico screening now enabling the evaluation of millions of compounds within weeks, compared to years using conventional experimental methods. Quantitative structure–activity relationship (QSAR) modeling, ADMET prediction, molecular dynamics simulations, and in silico toxicity profiling further refine chemical scaffolds, improving potency, selectivity, and drug-likeness while minimizing late-stage attrition. Recent breakthroughs, including AI/ML-driven predictive models, deep learning–based de novo molecule generation, fragment-based drug design, and omics-guided target discovery, have enhanced hit-to-lead success rates and preclinical translation efficiency. Despite these advances, challenges such as data quality, model interpretability, and accurate prediction of complex biological responses remain critical barriers. This review highlights the fundamental principles, recent technological developments, and emerging trends in molecular drug design and lead identification, and discusses future perspectives, including integrated AI-driven platforms, digital twins, and multi-omics-informed design strategies, which are expected to further improve precision, efficiency, and success rates in rational drug discovery and development.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.