用AI自动设计分子动力学模拟流程，发现高亲和力新化合物。
MDForge: Agentic Molecular Dynamics Pipeline Design under Sparse Simulator Feedback
- AI通过多智能体辩论动态优化代码生成，应对稀疏反馈。
- 在三个基准测试中性能媲美人类专家，发现新型皮摩尔级结合剂。
- 适合需要高效模拟设计的化学与药物研发人员。
分子动力学（MD）是基于第一性原理模拟分子行为的标准计算方法。设计针对新体系的MD流程需大量专业知识：即使运行一个分子也成本高昂，难以试错。我们利用大语言模型（LLM）代理自动化这一专家流程。不同于现有仅调度预设工具集的MD代理，我们将流程设计视为开放式的代码生成任务，通过口头奖励在线重塑代理行为。具体而言，构建了MDForge——一个基于上下文更新规则的LLM代理，通过物理专家多智能体辩论机制，将稀疏奖励密度化。在三个SAMPL宿主-客体结合自由能基准测试中，MDForge自动设计的流程性能可与人类专家媲美。部署于未见候选客体库后，其针对CB[7]的流程发现了新型结合剂，湿实验核磁共振验证确认其为高亲和力、皮摩尔级结合剂。数据与代码已开源：https://github.com/Zehong-Wang/MDForge。
原文摘要 · Abstract (English)
Molecular dynamics (MD) is the canonical in-silico method for atomistic molecular science, simulating molecular behavior from first-principle physics. Designing an MD pipeline for a new system requires substantial expert knowledge: running it on even one molecule is expensive, ruling out trial-and-error. We automate this expert pipeline-design process with an LLM agent. Unlike existing MD agents that orchestrate a predefined tool set, we treat pipeline design as open-ended code generation in which the agent's behavior is reshaped online by verbal reward. Specifically, we build MDForge, an LLM agent whose in-context update rule densifies the sparse reward via a multi-agent debate among physics experts. On three SAMPL host-guest binding free-energy benchmarks, MDForge automatically designs MD pipelines competitive with human experts. Deployed on a library of unseen candidate guests, its CB[7] pipeline discovers a novel binder that wet-lab competition NMR confirms is a high-affinity, picomolar CB[7] binder. Our data and code are available at https://github.com/Zehong-Wang/MDForge.
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