# Automating MD simulations for Proteins using Large language Models: NAMD-Agent

arXiv: https://arxiv.org/abs/2507.07887 · HTML: https://mlsanity.com/paper/2507.07887/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 55 references that are in the map.
- First posted on arXiv 10 Jul 2025.

## Builds on (most cited first, 16 of 16)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Toolformer: Language Models Can Teach Themselves to Use Tools](https://mlsanity.com/paper/2302.04761/) (2023)
- [Code Llama: Open Foundation Models for Code](https://mlsanity.com/paper/2308.12950/) (2023)
- [Augmented Language Models: a Survey](https://mlsanity.com/paper/2302.07842/) (2023)
- [Adapted Large Language Models Can Outperform Medical Experts in Clinical Text Summarization](https://mlsanity.com/paper/2309.07430/) (2023)
- [Agent AI: Surveying the Horizons of Multimodal Interaction](https://mlsanity.com/paper/2401.03568/) (2024)
- [Agentic AI for Scientific Discovery: A Survey of Progress, Challenges, and Future Directions](https://mlsanity.com/paper/2503.08979/) (2025)
- [AMGPT: a Large Language Model for Contextual Querying in Additive Manufacturing](https://mlsanity.com/paper/2406.00031/) (2024)
- [LLM-3D Print: Large Language Models To Monitor and Control 3D Printing](https://mlsanity.com/paper/2408.14307/) (2024)
- [LLM-guided Chemical Process Optimization with a Multi-Agent Approach](https://mlsanity.com/paper/2506.20921/) (2025)
- [Adsorb-Agent: Autonomous Identification of Stable Adsorption Configurations via Large Language Model Agent](https://mlsanity.com/paper/2410.16658/) (2024)
- [NANOGPT: A Query-Driven Large Language Model Retrieval-Augmented Generation System for Nanotechnology Research](https://mlsanity.com/paper/2502.20541/) (2025)
- [LLM-Drone: Aerial Additive Manufacturing with Drones Planned Using Large Language Models](https://mlsanity.com/paper/2503.17566/) (2025)

## Built on by (most cited first, 12 of 12)

- [CASCADE: Cumulative Agentic Skill Creation through Autonomous Development and Evolution](https://mlsanity.com/paper/2512.23880/) (2025)
- [El Agente Quntur: A research collaborator agent for quantum chemistry](https://mlsanity.com/paper/2602.04850/) (2026)
- [DynaMate: An Autonomous Agent for Protein-Ligand Molecular Dynamics Simulations](https://mlsanity.com/paper/2512.10034/) (2025)
- [PolyJarvis: An LLM-Orchestrated Agent for Automated All-Atom Molecular Dynamics of Amorphous Homopolymers](https://mlsanity.com/paper/2604.02537/) (2026)
- [Agentic Additive Manufacturing Alloy Evaluation](https://mlsanity.com/paper/2510.02567/) (2025)
- [MDGYM: Benchmarking AI Agents on Molecular Simulations](https://mlsanity.com/paper/2605.08941/) (2026)
- [Polymer-Agent: Large Language Model Agent for Polymer Design](https://mlsanity.com/paper/2601.16376/) (2026)
- [MDForge: Agentic Molecular Dynamics Pipeline Design under Sparse Simulator Feedback](https://mlsanity.com/paper/2606.12916/) (2026)
- [From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry](https://mlsanity.com/paper/2605.03205/) (2026)
- [Meta-Learning for Cross-Task Generalization in Protein Mutation Property Prediction](https://mlsanity.com/paper/2510.20943/) (2025)
- [In-situ process monitoring for defect detection in wire-arc additive manufacturing: an agentic AI approach](https://mlsanity.com/paper/2604.09889/) (2026)
- [Material Database Agent: A Multimodal Agentic Framework for Scientific Literature Mining](https://mlsanity.com/paper/2605.04278/) (2026)

## Abstract (arXiv)

Molecular dynamics (MD) simulations are essential for understanding protein structure, dynamics, and function, but preparing, running, and analyzing simulations remains time-consuming and error-prone. We present an automated pipeline that combines large language model (LLM) agents with Python scripting and HTMD MCP tools to generate simulation-ready inputs for NAMD3/CHARMM, execute simulations, analyze outputs, and recover from build or runtime failures. The framework was evaluated across five biomolecular system classes: a protein-DNA complex (p53 DNA-binding domain bound to its response element), a protein-membrane system (M2 muscarinic receptor with iperoxo in a POPC/cholesterol bilayer), a protein-ligand series (five congeneric TYK2 inhibitors), a protein-water reference (ubiquitin), and a protein-protein complex (barnase-barstar). For the protein-DNA system, the automated workflow reproduced key metrics from an independent published benchmark. To distinguish framework performance from model-specific behavior, we repeated the complete protein-DNA study using three LLM orchestrators: Claude Opus-4.8, GPT-5.6 Sol, and Nemotron 3 Ultra. All three completed the workflow, but they differed in benchmark-ranking fidelity and by up to two orders of magnitude in token consumption and cost. Across all system classes, the agent recovered experimentally and computationally established behavior. Additional post-processing software was used to refine simulation outputs, enabling a complete and largely hands-free workflow. This approach reduces setup effort, limits manual errors, supports parallel handling of diverse biomolecular systems, and provides a robust, adaptable foundation for LLM-driven automation in computational structural biology.