用大模型自动完成分子动力学模拟全流程，提升科研效率。
MDCrow: Automating Molecular Dynamics Workflows with Large Language Models
- 构建智能代理MDCrow，通过40个专家设计工具链自动化处理模拟任务。
- GPT-4o在25项任务中表现稳定，错误率低，性能优于多数开源模型。
- 适合需要自动化分子模拟的生物化学研究者快速上手使用。
分子动力学（MD）模拟对理解生物分子系统至关重要，但自动化仍具挑战。本文提出MDCrow，一个基于大语言模型（LLM）的智能代理，可自动完成MD工作流。MDCrow利用链式思维策略，集成超过40个专家设计的工具，涵盖文件处理、模拟设置、结果分析以及文献与数据库信息检索。我们在25项不同复杂度的任务上评估了MDCrow的性能，并测试其对任务难度和提示风格的鲁棒性。结果显示，gpt-4o在复杂任务中表现出低方差与高成功率，紧随其后的是性能出色的开源模型llama3-405b。尽管提示风格对大型模型影响较小，但显著影响小型模型的表现。
原文摘要 · Abstract (English)
Molecular dynamics (MD) simulations are essential for understanding biomolecular systems but remain challenging to automate. Recent advances in large language models (LLM) have demonstrated success in automating complex scientific tasks using LLM-based agents. In this paper, we introduce MDCrow, an agentic LLM assistant capable of automating MD workflows. MDCrow uses chain-of-thought over 40 expert-designed tools for handling and processing files, setting up simulations, analyzing the simulation outputs, and retrieving relevant information from literature and databases. We assess MDCrow's performance across 25 tasks of varying required subtasks and difficulty, and we evaluate the agent's robustness to both difficulty and prompt style. \texttt{gpt-4o} is able to complete complex tasks with low variance, followed closely by \texttt{llama3-405b}, a compelling open-source model. While prompt style does not influence the best models' performance, it has significant effects on smaller models.
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