Gaps between medical biology and AI drug discovery

Drug discovery is a complex, iterative process spanning biology, chemistry, pharmacology, and computational sciences. Artificial intelligence (AI) can accelerate this process but often misaligns with biological realities. Here, we highlight three crucial gaps in AI-driven drug discovery. First, conflating binding affinity with bioactivity ignores distinct experimental setups. Second, reliance on simplified bioactivity metrics limits the ability of AI models to capture richer biological contexts. Third, AI applications remain fragmented, addressing isolated tasks rather than integrating insights across pipeline stages. To bridge these gaps, we propose a biologically contextualized AI framework and provide guidelines for researchers in both medical biology and AI drug discovery.