lenhsherr/htmd

High throughput molecular dynamics simulations

HTMD: Programming Environment for Molecular Discovery

HTMD (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 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.

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.

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

Support and Development

Please report bugs via GitHub 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

Citing HTMD

If you use HTMD in your publication please cite:

Main citation

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

Other citations

Stefan Doerr, Toni Giorgino, Gerard Martínez-Rosell, João M. Damas, and Gianni De Fabritiis. High-Throughput Automated Preparation and Simulation of Membrane Proteins with HTMD. Journal of Chemical Theory and Computation, 2017, 13 (9), pp 4003–4011. doi:10.1021/acs.jctc.7b00480