HTMD: High-Throughput Molecular Dynamics for Molecular Discovery
Autor: | Frank Noé, Stefan Doerr, Matthew J. Harvey, G. De Fabritiis |
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Rok vydání: | 2016 |
Předmět: |
0301 basic medicine
Adaptive sampling Theoretical computer science Markov chain Computer science business.industry Test data generation Cloud computing Workspace Python (programming language) Computer Science Applications Visualization Computational science 03 medical and health sciences 030104 developmental biology Physical and Theoretical Chemistry business Cluster analysis computer computer.programming_language |
Zdroj: | Journal of Chemical Theory and Computation. 12:1845-1852 |
ISSN: | 1549-9626 1549-9618 |
Popis: | 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. |
Databáze: | OpenAIRE |
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