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

Abstract: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.

Computation and Language (cs.CL); Computational Engineering, Finance, and Science (cs.CE); Biomolecules (q-bio.BM)

arXiv:2507.07887 [cs.CL]

(or arXiv:2507.07887v2 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2507.07887 Focus to learn more arXiv-issued DOI via DataCite

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