用智能代理自动完成蛋白-配体分子动力学模拟全流程。
DynaMate: An Autonomous Agent for Protein-Ligand Molecular Dynamics Simulations
- 构建多智能体系统,自主规划、执行和纠错分子动力学流程。
- 在12个基准系统上成功完成全周期模拟,自动生成结合能分析结果。
- 适合药物设计与生物分子研究者,显著降低计算门槛。
基于力场的分子动力学(MD)模拟是研究生物分子结构、动态与功能的重要工具,广泛应用于药物发现与蛋白质工程。然而,其设置过程涉及参数化、输入准备与软件配置,技术复杂度高,限制了高效使用。尽管代理型大模型已展现自主执行多步骤科学任务的能力,但尚未成功应用于蛋白-配体MD工作流自动化。本文提出DynaMate,一个模块化多智能体框架,可自主设计并执行蛋白及蛋白-配体系统的完整MD流程,并通过MM/PB(GB)SA方法提供结合自由能计算。该框架集成动态工具调用、网络搜索、PaperQA与自我修正机制。DynaMate包含三个专用模块,协同完成实验规划、模拟执行与结果分析。我们在12个不同复杂度的基准系统上评估其性能,涵盖成功率、效率与适应性。DynaMate可靠完成了全周期模拟,通过迭代推理纠正运行时错误,并生成有意义的蛋白-配体相互作用分析。该自动化框架为未来生物分子与药物设计应用提供了标准化、可扩展、高效的时间节约型建模管线。
原文摘要 · Abstract (English)
Force field-based molecular dynamics (MD) simulations are indispensable for probing the structure, dynamics, and functions of biomolecular systems, including proteins and protein-ligand complexes. Despite their broad utility in drug discovery and protein engineering, the technical complexity of MD setup, encompassing parameterization, input preparation, and software configuration, remains a major barrier for widespread and efficient usage. Agentic LLMs have demonstrated their capacity to autonomously execute multi-step scientific processes, and to date, they have not successfully been used to automate protein-ligand MD workflows. Here, we present DynaMate, a modular multi-agent framework that autonomously designs and executes complete MD workflows for both protein and protein-ligand systems, and offers free energy binding affinity calculations with the MM/PB(GB)SA method. The framework integrates dynamic tool use, web search, PaperQA, and a self-correcting behavior. DynaMate comprises three specialized modules, interacting to plan the experiment, perform the simulation, and analyze the results. We evaluated its performance across twelve benchmark systems of varying complexity, assessing success rate, efficiency, and adaptability. DynaMate reliably performed full MD simulations, corrected runtime errors through iterative reasoning, and produced meaningful analyses of protein-ligand interactions. This automated framework paves the way toward standardized, scalable, and time-efficient molecular modeling pipelines for future biomolecular and drug design applications.
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