AI-powered medicinal chemistry and translational drug development.

Abstract

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. We outline the principles of major AI modalities and detail their roles in target discovery, virtual screening, molecular property prediction, de novo molecular design, fragment-based optimization, safety and absorption, distribution, metabolism, excretion, and toxicity (ADMET) assessment, and clinical trial design. We highlight how multimodal data fusion, predictive modeling, and human-AI collaborative frameworks are supporting more informed decisions in rational drug design. At the same time, we critically assess the limitations that constrain real-world impact, including data scarcity and inconsistency, model generalizability and interpretability, evolving regulatory expectations, and the persistent gap between in silico predictions and experimentally validated drug candidates. While a small but growing number of AI-guided molecules have entered clinical development, systematic evidence on whether AI-driven approaches ultimately deliver better drugs or faster timelines than traditional methods is still accruing. We discuss emerging opportunities at the intersection of AI with automation, robotics, multimodal biology, protein structure prediction, and autonomous discovery. With rigorous validation, high-quality datasets, and appropriate regulatory frameworks, AI can become a dependable tool for discovering safer, more effective, and more personalized medicines.