%0 Journal Article %1 Doerr2016HTMD %A Doerr, S. %A Harvey, M. J. %A Noé, Frank %A De Fabritiis, G. %D 2016 %J Journal of Chemical Theory and Computation %K molecular-dynamics molecular-modelling python software %N 4 %P 1845-1852 %R 10.1021/acs.jctc.6b00049 %T HTMD: High-Throughput Molecular Dynamics for Molecular Discovery %U http://dx.doi.org/10.1021/acs.jctc.6b00049 %V 12 %X Recent advances in molecular simulations have allowed scientists to investigate slower biological processes than ever before. Together with these advances came an explosion of data that has transformed a traditionally computing-bound into a data-bound problem. Here, we present HTMD, a programmable, extensible platform written in Python that aims to solve the data generation and analysis problem as well as increase reproducibility by providing a complete workspace for simulation-based discovery. So far, HTMD includes system building for CHARMM and AMBER force fields, projection methods, clustering, molecular simulation production, adaptive sampling, an Amazon cloud interface, Markov state models, and visualization. As a result, a single, short HTMD script can lead from a PDB structure to useful quantities such as relaxation time scales, equilibrium populations, metastable conformations, and kinetic rates. In this paper, we focus on the adaptive sampling and Markov state modeling features.

@article{Doerr2016HTMD, abstract = { Recent advances in molecular simulations have allowed scientists to investigate slower biological processes than ever before. Together with these advances came an explosion of data that has transformed a traditionally computing-bound into a data-bound problem. Here, we present HTMD, a programmable, extensible platform written in Python that aims to solve the data generation and analysis problem as well as increase reproducibility by providing a complete workspace for simulation-based discovery. So far, HTMD includes system building for CHARMM and AMBER force fields, projection methods, clustering, molecular simulation production, adaptive sampling, an Amazon cloud interface, Markov state models, and visualization. As a result, a single, short HTMD script can lead from a PDB structure to useful quantities such as relaxation time scales, equilibrium populations, metastable conformations, and kinetic rates. In this paper, we focus on the adaptive sampling and Markov state modeling features. }, added-at = {2017-02-27T23:25:32.000+0100}, author = {Doerr, S. and Harvey, M. J. and Noé, Frank and De Fabritiis, G.}, biburl = {https://www.bibsonomy.org/bibtex/20f9388b35f15e77bba1ef9b61e059059/salotz}, description = {HTMD: High-Throughput Molecular Dynamics for Molecular Discovery - Journal of Chemical Theory and Computation (ACS Publications)}, doi = {10.1021/acs.jctc.6b00049}, eprint = {http://dx.doi.org/10.1021/acs.jctc.6b00049}, interhash = {c95697d22aa596726f5316f025644747}, intrahash = {0f9388b35f15e77bba1ef9b61e059059}, journal = {Journal of Chemical Theory and Computation}, keywords = {molecular-dynamics molecular-modelling python software}, note = {PMID: 26949976}, number = 4, pages = {1845-1852}, timestamp = {2017-02-27T23:25:32.000+0100}, title = {HTMD: High-Throughput Molecular Dynamics for Molecular Discovery}, url = {http://dx.doi.org/10.1021/acs.jctc.6b00049}, volume = 12, year = 2016 }