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Evaluating Agentic AI Gaps in Drug Discovery

Evaluating Agentic AI Gaps in Drug Discovery

Researchers evaluate agentic systems for drug discovery across 15 task classes, identifying five key capability gaps like lack of protein models and safety trade-offs. A knowledge-probing experiment reveals architectural bottlenecks in current frameworks. They propose design requirements and a capability matrix for next-gen systems.

ArXiv AIResearchFeb 12#research#beyond-smiles#v1