MDCrow: Automating Molecular Dynamics Workflows with Large Language Models
This work addresses the problem of automating complex molecular dynamics simulations for researchers in computational biology, representing an incremental improvement by applying existing LLM methods to a specific domain.
The authors tackled the challenge of automating molecular dynamics workflows by introducing MDCrow, an LLM-based agent that uses expert-designed tools to handle tasks like simulation setup and analysis, achieving completion of complex tasks with low variance using models like GPT-4o and Llama3-405b.
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.