Artificial intelligence in drug discovery — what it is, where we stand and the path forward
Summary
The article surveys AI's role in drug discovery, highlighting progress but emphasizing a gap between model validation and real-world impact. It identifies bottlenecks such as translation to clinical practice, data complexity, and underspecified models, and proposes benchmarking shifts toward end-to-end translational outcomes and decision-making improvements.