Artificial intelligence in drug discovery: Integrating data, models, and mechanisms for next-generation therapeutics

Artificial intelligence (AI) offers strong promise for accelerating drug discovery, from target identification to lead optimization and toxicity prediction. Over the past decade it has produced real advances in protein structure prediction, billion-compound virtual screening, and generative molecular design. And yet, despite these advances, the translation of AI-predicted compounds into clinically successful drugs remains limited. No drug discovered primarily through AI has reached the market, indicating that the most consequential gaps lie in clinical translation rather than in early-stage design. This review provides a critical synthesis of the past decade’s progress in AI for drug discovery, analyzing tools such as machine learning, generative models, and structure-based design. Key breakthroughs such as AlphaFold, Deep Docking, and GENTRL are highlighted alongside persistent limitations, including data quality and scarcity, limited interpretability, weak generalization, and a shortage of prospective wet-lab validation. We further explore emerging strategies like foundation models, multimodal learning, and active learning that aim to narrow these gaps. By juxtaposing technological achievements with the data, validation, and ethical challenges that still constrain the field, this review offers a realistic roadmap for the responsible and measured integration of AI in pharmaceutical science.