ct6b00049_si_001.pdf (366.01 kB)
HTMD: High-Throughput Molecular Dynamics for Molecular Discovery
journal contribution
posted on 2016-03-07, 00:00 authored by S. Doerr, M. J. Harvey, Frank Noé, G. De FabritiisRecent 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.