title: HTMD: High-Throughput Molecular Dynamics for Molecular Discovery.
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.
authors.affiliation: Computational Biophysics Laboratory (GRIB-IMIM), Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB) , C/Doctor Aiguader 88, 08003 Barcelona, Spain.
authors.forename: S
authors.initials: S
authors.lastname: Doerr
authors.affiliation: Acellera, Barcelona Biomedical Research Park (PRBB) , C/Doctor Aiguader 88, 08003 Barcelona, Spain.
authors.forename: M J
authors.initials: MJ
authors.lastname: Harvey
authors.affiliation: Department of Mathematics, Computer Science and Bioinformatics, Free University of Berlin , Berlin, Germany.
authors.forename: Frank
authors.initials: F
authors.lastname: Noé
authors.affiliation: Institució Catalana de Recerca i Estudis Avançats (ICREA) , Passeig Lluis Companys 23, Barcelona 08010, Spain.
authors.forename: G
authors.initials: G
authors.lastname: De Fabritiis