Differentiable Rotamer Sampling with Molecular Force Fields
Fuente:
arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2023
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| _version_ | 1866915350240559104 |
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| author | Sha, Congzhou M. Wang, Jian Dokholyan, Nikolay V. |
| author_facet | Sha, Congzhou M. Wang, Jian Dokholyan, Nikolay V. |
| contents | Molecular dynamics is the primary computational method by which modern structural biology explores macromolecule structure and function. Boltzmann generators have been proposed as an alternative to molecular dynamics, by replacing the integration of molecular systems over time with the training of generative neural networks. This neural network approach to MD samples rare events at a higher rate than traditional MD, however critical gaps in the theory and computational feasibility of Boltzmann generators significantly reduce their usability. Here, we develop a mathematical foundation to overcome these barriers; we demonstrate that the Boltzmann generator approach is sufficiently rapid to replace traditional MD for complex macromolecules, such as proteins in specific applications, and we provide a comprehensive toolkit for the exploration of molecular energy landscapes with neural networks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2302_11430 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Differentiable Rotamer Sampling with Molecular Force Fields Sha, Congzhou M. Wang, Jian Dokholyan, Nikolay V. Computational Physics Artificial Intelligence Biological Physics Molecular dynamics is the primary computational method by which modern structural biology explores macromolecule structure and function. Boltzmann generators have been proposed as an alternative to molecular dynamics, by replacing the integration of molecular systems over time with the training of generative neural networks. This neural network approach to MD samples rare events at a higher rate than traditional MD, however critical gaps in the theory and computational feasibility of Boltzmann generators significantly reduce their usability. Here, we develop a mathematical foundation to overcome these barriers; we demonstrate that the Boltzmann generator approach is sufficiently rapid to replace traditional MD for complex macromolecules, such as proteins in specific applications, and we provide a comprehensive toolkit for the exploration of molecular energy landscapes with neural networks. |
| title | Differentiable Rotamer Sampling with Molecular Force Fields |
| topic | Computational Physics Artificial Intelligence Biological Physics |
| url | https://arxiv.org/abs/2302.11430 |