AI-powered medicinal chemistry and translational drug development

Medicinal chemistry sits at the center of modern drug discovery, yet translating molecular designs into approved
medicines remains slow, expensive, and prone to high attrition across the pipeline from target identification to
clinical validation. Artificial intelligence (AI) is beginning to reshape this landscape by enabling large-scale integration,
interpretation, and generation of chemical, biological, and clinical data for hypothesis generation, chemical space
exploration, and iterative cycles of model-guided design and experimental validation. In this review, we examine
how machine learning, deep learning, natural language processing (NLP), and generative modeling are being
applied across medicinal chemistry and drug development...READ ARTICLE