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.
Key Points
- 1.Identifies gaps in peptide support, in vivo bridging, and multi-objective optimization
- 2.Frontier LLMs capable but frameworks don't expose peptide reasoning
- 3.Proposes capability matrix for resource-constrained agentic frameworks
Impact Analysis
Highlights limitations in current AI drug discovery tools, paving way for more robust, generalizable systems that handle real-world constraints and trade-offs.
Technical Details
Evaluates six frameworks on peptide therapeutics and in vivo tasks; pairs with LLM probing on small molecules vs. peptides.
