Artificial Intelligence in Drug Discovery: Translational Bottlenecks, Biomedical Engineering Constraints, and Commercial Realities

Abstract

Artificial intelligence (AI) has emerged as a major technological development influencing modern pharmaceutical research and development. Machine learning, computational biology, and large-scale biological data analysis have the potential to improve target identification, molecular optimization, and clinical development. However, despite growing enthusiasm, significant uncertainty remains regarding the ability of AI systems to overcome the biological, regulatory, and translational challenges that have historically limited pharmaceutical innovation. This review examines AI-driven drug discovery from a biomedical engineering and translational perspective. It evaluates the scientific foundations of AIenabled pharmaceutical development, including target identification, molecular design, and multimodal biological modeling, while analyzing key barriers involving biological complexity, clinical translation, regulatory oversight, and commercialization. Case studies of Recursion Pharmaceuticals and Schrödinger demonstrate both the opportunities and limitations associated with integrating computational approaches into therapeutic development. The analysis suggests that AI will become an increasingly important component of pharmaceutical workflows; however, long-term impact will depend less on algorithmic advancement alone and more on effective integration with biological validation, experimental rigor, clinical evidence, and scalable translational infrastructure. AI should therefore be viewed as an enabling technology that enhances decision-making and prioritization rather than a replacement for traditional biomedical research processes.