AgentFold: Closed-Loop Agentic Search for Protein Folding Model Design
Scientific LLM agents have shown promise in literature reasoning, tool use, and experiment planning, but it remains unclear whether they can autonomously improve large, tightly coupled scientific machine-learning systems through executable code changes and computationally expensive validation. We study this question in protein folding, where progress requires coordinated architectural modifications, multi-objective evaluation, and domain-aware interpretation. We present AgentFold, a multi-agent framework that formulates folding-model development as a closed-loop search over executable code variants. Starting from ESMFold, AgentFold proposes hypotheses, implements and debugs code-level mod...
| Authors | Mingquan Liu, Jiangyu Chen, Hanqun Cao et al. |
|---|---|
| Primary category | cs.AI |
| Published | 27 August 2026 |
| Last updated at source | 28 August 2026 |
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