The DOI matches its Crossref record; 52 of the 52 references it cites were found in a registry or an index.
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.
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Scikit-learn: Machine Learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016
VMD: visual molecular dynamics
Humphrey, W., Dalke, A., & Schulten, K. (1996) · Journal of Molecular Graphics · doi:10.1016/0263-7855(96)00018-5 · Cited by 66,952
Comparison of multiple Amber force fields and development of improved protein backbone parameters
Hornak, V., Abel, R., Okur, A., Strockbine, B., Roitberg, A., & Simmerling, C. (2006) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.21123 · Cited by 6,914
Improved side‐chain torsion potentials for the Amber ff99SB protein force field
Lindorff‐Larsen, K., Piana, S., Palmo, K., Maragakis, P., Klepeis, J. L., Dror, R. O., & Shaw, D. E. (2010) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.22711 · Cited by 6,134
MDAnalysis: A toolkit for the analysis of molecular dynamics simulations
Michaud‐Agrawal, N., Denning, E. J., Woolf, T. B., & Beckstein, O. (2011) · Journal of Computational Chemistry · doi:10.1002/jcc.21787 · Cited by 4,486
The Singularity Is near: When Humans Transcend Biology
Mead, W. R., & Kurzweil, R. (2006) · Foreign Affairs · doi:10.2307/20031996 · Cited by 3,429
MDTraj: A Modern Open Library for the Analysis of Molecular Dynamics Trajectories
McGibbon, R. T., Beauchamp, K. A., Harrigan, M. P., Klein, C., Swails, J. M., Hernández, C. X., Schwantes, C. R., Wang, L.-P., Lane, T. J., & Pande, V. S. (2015) · Biophysical Journal · doi:10.1016/j.bpj.2015.08.015 · Cited by 2,373
How fast-folding proteins fold
Lindorff-Larsen, K., Piana, S., Dror, R. O., & Shaw, D. E. (2011) · Science · doi:10.1126/science.1208351 · Cited by 1,980
Protein Structure Prediction Using Rosetta
Rohl, C. A., Strauss, C. E. M., Misura, K. M. S., & Baker, D. (2004) · Methods in Enzymology · doi:10.1016/s0076-6879(04)83004-0 · Cited by 1,725
Markov models of molecular kinetics: Generation and validation
Prinz, J.-H., Wu, H., Sarich, M., Keller, B., Senne, M., Held, M., Chodera, J. D., Schütte, C., & Noé, F. (2011) · The Journal of Chemical Physics · doi:10.1063/1.3565032 · Cited by 1,275
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Cited by
Parrish, R. M., Burns, L. A., Smith, D. G. A., Simmonett, A. C., DePrince, A. E., Hohenstein, E. G., Bozkaya, U., Sokolov, A. Y., Di Remigio, R., Richard, R. M., et al. (2017) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.7b00174 · Cited by 1,249
Mol* Viewer: modern web app for 3D visualization and analysis of large biomolecular structures
Sehnal, D., Bittrich, S., Deshpande, M., Svobodová, R., Berka, K., Bazgier, V., Velankar, S., Burley, S. K., Koča, J., & Rose, A. S. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab314 · Cited by 1,154
ProtGPT2 is a deep unsupervised language model for protein design
Ferruz, N., Schmidt, S., & Höcker, B. (2022) · Nature Communications · doi:10.1038/s41467-022-32007-7 · Cited by 795
CHARMM-GUI supports the Amber force fields
Lee, J., Hitzenberger, M., Rieger, M., Kern, N. R., Zacharias, M., & Im, W. (2020) · The Journal of Chemical Physics · doi:10.1063/5.0012280 · Cited by 428
Time-lagged autoencoders: Deep learning of slow collective variables for molecular kinetics
Wehmeyer, C., & Noé, F. (2018) · The Journal of Chemical Physics · doi:10.1063/1.5011399 · Cited by 401
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Metadata
- Authors
- Doerr, S., Harvey, M. J., Noé, F., & De Fabritiis, G.
- Venue
- Journal of Chemical Theory and Computation
- Publisher
- American Chemical Society (ACS)
- Published
- 2016-03-16
- Volume
- 12
- Issue
- 4
- Pages
- 1845-1852
- ISSN
- 1549-9618 (ISSN-L)
- 1549-9626
- OpenAlex
- W2296686360
- Cited by
- 454
- References
- 52
- FWCI
- 15.65
- Type
- journal article
- Language
- en
- Open access
- closed
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