Has AI Reshaped Drug Discovery, or Is There Still a Long Way to Go?

The conventional drug discovery pipeline is labour-intensive, time-consuming, and costly, involving target identification, hit discovery, lead optimization, and extensive preclinical and clinical evaluation. To overcome these limitations, artificial intelligence (AI) has emerged as a transformative tool in drug discovery, gaining widespread adoption in the pharmaceutical industry during the 2010s due to advances in computing power, data availability, and deep learning. AI-based approaches, including molecular property prediction, protein structure modelling, natural language processing, and ADME/Tox prediction, have enhanced efficiency, reduced costs, and improved decision-making across multiple stages of drug development. Several AI-guided molecules have progressed into clinical trials, with encouraging early-phase success rates, highlighting the potential of AI to accelerate innovation. However, despite more than a decade of intensive research, no AI-only originated drug has yet achieved full regulatory approval, reflecting persistent challenges consistent with Eroom's law. Key limitations include poor data quality and accessibility, lack of model interpretability, gaps between computational predictions and chemical feasibility, and the inherent complexity of biological systems that limit translational success. Furthermore, AI-driven hypothesis generation does not replace the need for scientific reasoning and experimental validation. Overall, while AI has significantly accelerated early drug discovery stages, it remains a supportive tool rather than a standalone solution, underscoring the continued need for human expertise and experimental research.