Computer-Aided Drug Design in the Artificial Intelligence Era: Integrated Workflows, Validation, and Translational Progress
Keywords:
Computer-aided Drug Design, Molecular Docking, QSAR, Pharmacophore, Molecular Dynamics, ADMETAbstract
Computer-aided drug design (CADD) has developed from a supporting computational approach into an integrated component of modern drug discovery. Structure-based and ligand-based methods, including molecular docking, virtual screening, quantitative structure-activity relationship (QSAR) modelling, pharmacophore modelling, molecular dynamics (MD) simulations and in silico ADMET assessment, can reduce the experimental search space and improve lead prioritization. Artificial intelligence (AI) now extends these workflows through data-driven target identification, biomolecular structure prediction, molecular-property prediction and generative molecular design. However, computational performance does not by itself establish therapeutic activity. Docking scores are approximate, MD results depend on force fields and sampling, QSAR/AI models may fail outside their applicability domain, and predicted ADMET properties require experimental confirmation. This focused review summarizes the principal CADD methods, shows how they can be combined in a practical screening-to-validation workflow, and discusses current AI-enabled advances. The clinical translation of rentosertib, a generative-AI-designed TNIK inhibitor for idiopathic pulmonary fibrosis, is highlighted as an instructive example. The central conclusion is that the strongest drug-discovery strategy is not AI or classical CADD alone, but an iterative combination of computational prediction, chemical reasoning and rigorous experimental validation.
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