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 talk, we focus on adaptive sampling, Markov state modeling features, automatic ligand parameterization and protein preparation. HTMD is currently used by many academic groups and several pharma companies worldwide. Website: http://www.htmd.org

HTMD: High-throughput molecular dynamics for molecular discovery

253rd National Meeting (2017)

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