ArticleMachine learning: science and technology2026
MDCrow: automating molecular dynamics workflows with large language models.
Article in Machine learning: science and technology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Transforming Molecular Synthesis With Large Language Models.Chemistry (Weinheim an der Bergstrasse, Germany) · 2026Review
- Leveraging Large Language Models for Understanding Fundamental Principles of Catalysis.The journal of physical chemistry. C, Nanomaterials and interfaces · 2026Review
- i-gRINN: a next-generation web server for protein energy network analysis of heterogeneous biomolecular systems with natural language-based data exploration.Nucleic acids research · 2026Article
- Organic Chemistry as a Catalyst for AI Innovation: Challenges, Methods, and Emerging Paradigms.Chemical reviews · 2026Review
- AutoLabs: cognitive multi-agent systems with self-correction for autonomous chemical experimentation.Scientific reports · 2026Article
- MutexaGPT: An Intuition-to-Design Translator for Physics-based Enzyme Engineering.Research square · 2026Article
- Transforming Molecular Science With Large Language Models: From Molecule Understanding to Autonomous Scientific Discovery.Chemistry, an Asian journal · 2026Review
- General-Purpose Models for the Chemical Sciences: LLMs and Beyond.Chemical reviews · 2026Review
- Assessing the Chemical Intelligence of Large Language Models.Journal of chemical information and modeling · 2026Article
- ChemGraph as an agentic framework for computational chemistry workflows.Communications chemistry · 2026Article
- Gromacs MetaDump: a tool for extracting GROMACS simulation metadata.Journal of cheminformatics · 2025Article
- Generative Deep Learning for de Novo Drug Design─A Chemical Space Odyssey.Journal of chemical information and modeling · 2025Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Molecular dynamics (MD) simulations are essential for understanding biomolecular systems but remain challenging to automate. Recent advances in large language models (LLMs) 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 for proteins. 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 common tasks of varying complexity, and we evaluate the agent's robustness to difficulty and prompt style. gpt-4o is able to complete increasingly complex tasks with low variance, followed closely by 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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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.