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High throughput molecular dynamics (HTMD)

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# HTMD: Programming Environment for Molecular Discovery [HTMD](https://www.htmd.org) (acronym for High-Throughput Molecular Dynamics) is a programmable, extensible platform written in Python. It provides a complete workspace for simulation-based discovery through molecular simulations while aiming to solve the data generation and analysis problem as well as increase reproducibility.

## Licensing HTMD Community Edition is free to use for non-profit work. Contact Acellera [www.acellera.com/contact](https://www.acellera.com/contact/) for information on the full version HTMD Pro or if you need a different license.

## Download HTMD

### Using released versions HTMD is distributed through conda package manager. The instructions for downloading HTMD can be found in [https://software.acellera.com/download.html](https://software.acellera.com/download.html).

### Using this repository If you want to use this repository, we recommend to still download a released version of HTMD to have all dependencies and then set PYTHONPATH to the git directory.

## HTMD Documentation and User Guide For HTMD Documentation, please visit: [https://software.acellera.com/docs/latest/htmd/api.html](https://software.acellera.com/docs/latest/htmd/api.html).

For a User Guide (easy to start examples), please visit: [https://software.acellera.com/docs/latest/htmd/tutorials.html](https://software.acellera.com/docs/latest/htmd/tutorials.html)

## Support and Development

Please report bugs via [GitHub Issues](https://github.org/acellera/htmd/issues).

HTMD is an open-source software and we welcome contributions from the community. For more information on how to contribute to HTMD, please visit: [https://software.acellera.com/docs/latest/htmd/developers/howto.html](https://software.acellera.com/docs/latest/htmd/developers/howto.html)

## Citing HTMD

If you use HTMD in your publication please cite:

Stefan Doerr, Matthew J. Harvey, Frank Noé, and Gianni De Fabritiis. HTMD: High-throughput molecular dynamics for molecular discovery. Journal of Chemical Theory and Computation, 2016, 12 (4), pp 1845–1852. [doi:10.1021/acs.jctc.6b00049](http://pubs.acs.org/doi/abs/10.1021/acs.jctc.6b00049)

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