MDAgent: A Multi-Agent Framework for End-to-End Molecular Dynamics Research

TL;DR

MDAgent is a multi-agent system that automates and enhances end-to-end molecular dynamics research, integrating problem understanding, simulation, analysis, and knowledge transfer.

Contribution

It introduces a unified multi-agent framework with case-based learning for scalable, interpretable, and adaptable molecular dynamics research workflows.

Findings

Achieved stable end-to-end performance across multiple tasks.

Supported cross-task transfer of knowledge without retraining.

Successfully analyzed complex membrane protein conformational transitions.

Abstract

Molecular dynamics (MD) simulation is a powerful tool for studying biomolecular structural changes, molecular recognition, transmembrane transport, and functional mechanisms. However, its practical bottleneck lies not only in software operation or parameter setup, but in translating experimental questions into executable, interpretable, and reviewable computational workflows. Here, we present MDAgent, a multi-agent system for end-to-end molecular dynamics research. The system integrates problem understanding, literature-guided strategy design, simulation execution, trajectory analysis, mechanistic interpretation, and quality supervision into a unified workflow, enabling agents not only to run simulations but also to generate research-oriented computational plans and analytical reports. We further introduce a case-based learning mechanism based on Skill and Memory, which stores reusable knowledge from prior tasks, including parameter choices, operational rules, analytical logic, and problem-solving pathways, thereby supporting cross-task transfer without retraining the underlying model. Across multiple representative molecular simulation tasks, MDAgent achieved stable end-to-end performance with improved strategic adaptability, interpretability, and generalization. In an independent complex task involving conformational transitions of TMEM16F and XKR8, the system successfully completed system design, simulation, and mechanistic analysis for large membrane proteins. These results show that combining multi-agent collaboration with case-based learning can transform MD agents from workflow automation tools into scientific question-oriented computational research systems, providing a scalable framework for AI-driven automated research.