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TorchMD: A Deep Learning Framework for Molecular Simulations
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Molecular dynamics simulations provide a mechanistic description of molecules by relying on empirical potentials. The quality and transferability of such potentials can be improved leveraging data-driven models derived with machine learning approaches. Here, we present TorchMD, a framework for molecular simulations with mixed classical and machine learning potentials. All force computations including bond, angle, dihedral, Lennard-Jones, and Coulomb interactions are expressed as PyTorch arrays and operations. Moreover, TorchMD enables learning and simulating neural network potentials. We validate it using standard Amber all-atom simulations, learning an ab initio potential, performing an end-to-end training, and finally learning and simulating a coarse-grained model for protein folding. We believe that TorchMD provides a useful tool set to support molecular simulations of machine learning potentials. Code and data are freely available at github.com/torchmd.
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“The innovation in the development and integration of MD simulations with deep learning can reproduce, interpret, predict, and generate data relating to the behavior of biological macromolecules.”
From AlphaFold, Artificial Intelligence (AI), and Allostery (2022) · cites this paper as [188]
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“In another example, a deep learning framework with mixed classical and machine learning potentials (TorchMD) has been developed for molecular simulations.188”
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Metadata
- Authors
- Doerr, S., Majewski, M., Pérez, A., Krämer, A., Clementi, C., Noe, F., Giorgino, T., & De Fabritiis, G.
- Affiliations
- Acellera (Spain) (ES)
- Universitat Pompeu Fabra (ES)
- Freie Universität Berlin (DE)
- Rice University (US)
- Department of Mathematics and Computer Science, Freie Universität, 14195 Berlin, Germany
- Biophysics Institute, National Research Council (CNR-IBF), 20133 Milano, Italy
- Acellera, 08005 Barcelona, Spain
- ORCID
- Venue
- Journal of Chemical Theory and Computation
- Publisher
- American Chemical Society (ACS)
- Published
- 2021-03-17
- Volume
- 17
- Issue
- 4
- Pages
- 2355-2363
- ISSN
- 1549-9618 (ISSN-L)
- 1549-9626
- PMC
- PMC8486166
- OpenAlex
- W3113616921
- Cited by
- 230
- References
- 60
- Citations by year
- 2026: 30 · 2025: 51 · 2024: 52 · 2023: 61 · 2022: 24 · 2021: 12
- FWCI
- 11.31
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Journal of Chemical Theory and Computation (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Repositori digital de la UPF (Universitat Pompeu Fabra) (submittedVersion, cc-by)
- arXiv (Cornell University)
- Funders
- Chan Zuckerberg Initiative
- Horizon 2020 Framework Programme (823712)
- Topics
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Chemistry › Physical and Theoretical Chemistry › Advanced Physical and Chemical Molecular Interactions
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Advanced Chemical Physics Studies
- Keywords
- molecular dynamics, machine learning interatomic potentials, neural network potentials, Lennard-Jones interactions, electrostatic interactions, ab initio potentials, protein folding, coarse-grained models, PyTorch
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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