Comprehensive Approaches in Computer-Aided Drug Design: QSAR, Docking, Screening, Homology, Pharmacophore and AI-Driven Insights

Résumé fourni par la source

The evolution of computer-aided drug design (CADD) represents one of the most profound paradigms shifts in pharmaceutical research, transforming empirical, trial-and-error experimentation into a rational, hypothesis-driven scientific process. From the serendipitous discovery of penicillin in the early 20th century to the structure-based optimization of HIV protease inhibitors and the data-centric revolution driven by artificial intelligence, CADD has continually redefined how molecules are designed, analysed, and optimized for therapeutic efficacy. Pioneering developments such as the determination of myoglobin’s crystal structure, the advent of molecular mechanics and quantum calculations, and the introduction of the first quantitative structure–activity relationship (QSAR) models laid the groundwork for modern rational drug discovery. Over subsequent decades, methodologies such as molecular docking, pharmacophore modelling, homology modelling, molecular dynamics simulations, and multi-dimensional QSAR expanded the predictive capacity of in silico research. Contemporary CADD integrates big data analytics, deep learning, and multi-omics data, bridging molecular insights with systems pharmacology. This chapter traces the historical foundations of CADD, outlines its multidisciplinary underpinnings, and contrasts structure-based and ligand-based paradigms. It also evaluates how computational approaches have reshaped drug discovery economics, accelerated innovation, and set the stage for future AI-integrated, ethically governed, and open-science-driven discovery frameworks.

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