MD-LLM-1: A Large Language Model for Molecular Dynamics
Fuente:
arXiv
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912521897639936 |
|---|---|
| author | Murtada, Mhd Hussein Brotzakis, Z. Faidon Vendruscolo, Michele |
| author_facet | Murtada, Mhd Hussein Brotzakis, Z. Faidon Vendruscolo, Michele |
| contents | Molecular dynamics (MD) is a powerful approach for modelling molecular systems, but it remains computationally intensive on spatial and time scales of many macromolecular systems of biological interest. To explore the opportunities offered by deep learning to address this problem, we introduce a Molecular Dynamics Large Language Model (MD-LLM) framework to illustrate how LLMs can be leveraged to learn protein dynamics and discover states not seen in training. By applying MD-LLM-1, the first implementation of this approach, obtained by fine-tuning Mistral 7B, to the T4 lysozyme and Mad2 protein systems, we show that training on one conformational state enables the prediction of other conformational states. These results indicate that MD-LLM-1 can learn the principles for the exploration of the conformational landscapes of proteins, although it is not yet modeling explicitly their thermodynamics and kinetics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_03709 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MD-LLM-1: A Large Language Model for Molecular Dynamics Murtada, Mhd Hussein Brotzakis, Z. Faidon Vendruscolo, Michele Biomolecules Computation and Language Machine Learning Computational Physics Molecular dynamics (MD) is a powerful approach for modelling molecular systems, but it remains computationally intensive on spatial and time scales of many macromolecular systems of biological interest. To explore the opportunities offered by deep learning to address this problem, we introduce a Molecular Dynamics Large Language Model (MD-LLM) framework to illustrate how LLMs can be leveraged to learn protein dynamics and discover states not seen in training. By applying MD-LLM-1, the first implementation of this approach, obtained by fine-tuning Mistral 7B, to the T4 lysozyme and Mad2 protein systems, we show that training on one conformational state enables the prediction of other conformational states. These results indicate that MD-LLM-1 can learn the principles for the exploration of the conformational landscapes of proteins, although it is not yet modeling explicitly their thermodynamics and kinetics. |
| title | MD-LLM-1: A Large Language Model for Molecular Dynamics |
| topic | Biomolecules Computation and Language Machine Learning Computational Physics |
| url | https://arxiv.org/abs/2508.03709 |