Metadata-Version: 2.2
Name: torchmd
Version: 1.0.4
Summary: TorchMD. Molecular dynamics with pytorch
Author-email: Acellera <info@acellera.com>
License: Copyright (c) 2020 Acellera
        
        Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
        
        The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
        
        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
Project-URL: homepage, https://github.com/torchmd/torchmd/
Project-URL: repository, https://github.com/torchmd/torchmd/
Classifier: Operating System :: POSIX :: Linux
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Description-Content-Type: text/markdown
License-File: LICENSE.md
Requires-Dist: pyyaml
Requires-Dist: tqdm
Requires-Dist: moleculekit
Requires-Dist: pandas
Requires-Dist: networkx
Requires-Dist: scipy

# TorchMD

## About

TorchMD intends to provide a simple to use API for performing molecular dynamics using PyTorch. This enables researchers to more rapidly do research in force-field development as well as integrate seamlessly neural network potentials into the dynamics, with the simplicity and power of PyTorch.

TorchMD uses chemical units consistent with classical MD codes such as ACEMD, namely kcal/mol for energies, K for temperatures, g/mol for masses, and Å for distances.

TorchMD is currently WIP so feel free to provide feedback on the API or potential bugs in the GitHub issue tracker.

Also check TorchMD-Net for fast and accurate neural network potentials https://github.com/torchmd/torchmd-net/

## Citation

Please cite:

```
@misc{doerr2020torchmd,
      title={TorchMD: A deep learning framework for molecular simulations},
      author={Stefan Doerr and Maciej Majewsk and Adrià Pérez and Andreas Krämer and Cecilia Clementi and Frank Noe and Toni Giorgino and Gianni De Fabritiis},
      year={2020},
      eprint={2012.12106},
      archivePrefix={arXiv},
      primaryClass={physics.chem-ph}
}
```

To reproduce the paper go to the tutorial notebook https://github.com/torchmd/torchmd-cg/blob/master/tutorial/Chignolin_Coarse-Grained_Tutorial.ipynb

## License

Note. All the code in this repository is MIT, however we use several file format readers that are taken from Moleculekit which has a free open source non-for-profit, research license. This is mainly in torchmd/run.py. Moleculekit is installed automatically being in the requirement file. Check out Moleculekit here: https://github.com/Acellera/moleculekit

## Installation

We recommend installing TorchMD in a new python environment ideally through the Miniforge package manager.

```
mamba create -n torchmd
mamba activate torchmd
mamba install pytorch python=3.10 -c conda-forge
mamba install moleculekit parmed jupyter -c acellera -c conda-forge # For running the examples
pip install torchmd
```

## Examples

Various examples can be found in the `examples` folder on how to perform dynamics using TorchMD.

## Help and comments

Please use the github issue of this repository.

## Acknowledgements

We would like to acknowledge funding by the Chan Zuckerberg Initiative and Acellera in support of this project. This project will be now developed in collaboration with openMM (www.openmm.org) and acemd (www.acellera.com/acemd).
