Agentic BAIM-LLM Evaluation (ABLE): Benchmarking LLM Use of Protein Design Tools
We introduce ABLE, a benchmark for evaluating LLM agents' ability to use biological AI models (BAIMs), such as ProteinMPNN and AlphaFold3, in dual-use protein design workflows. ABLE assesses agent performance through a set of tasks spanning structure retrieval, sequence generation, and design validation. We evaluate 15 frontier models and find that seven refuse all tasks, while the remaining models exhibit substantial performance differences. Claude Sonnet 4 and Gemini 3 Pro achieve the highest scores across information retrieval, tool selection, and tool use. We further compare model performance on a subset of tasks against an expert human baseline. Our results suggest that current LLMs ...
| Authors | Bryce Cai, Geetha Jeyapragasan, Samira Nedungadi et al. |
|---|---|
| Primary category | cs.AI |
| Published | 5 September 2026 |
| Last updated at source | 5 September 2026 |
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