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Machine Learning Estimates of Natural Product Conformational Energies
Machine learning has been used for estimation of potential energy surfaces to speed up molecular dynamics simulations of small systems. We demonstrate that this approach is feasible for significantly larger, structurally complex molecules, taking the natural product Archazolid A, a potent inhibitor...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3894151/ https://www.ncbi.nlm.nih.gov/pubmed/24453952 http://dx.doi.org/10.1371/journal.pcbi.1003400 |
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author | Rupp, Matthias Bauer, Matthias R. Wilcken, Rainer Lange, Andreas Reutlinger, Michael Boeckler, Frank M. Schneider, Gisbert |
author_facet | Rupp, Matthias Bauer, Matthias R. Wilcken, Rainer Lange, Andreas Reutlinger, Michael Boeckler, Frank M. Schneider, Gisbert |
author_sort | Rupp, Matthias |
collection | PubMed |
description | Machine learning has been used for estimation of potential energy surfaces to speed up molecular dynamics simulations of small systems. We demonstrate that this approach is feasible for significantly larger, structurally complex molecules, taking the natural product Archazolid A, a potent inhibitor of vacuolar-type ATPase, from the myxobacterium Archangium gephyra as an example. Our model estimates energies of new conformations by exploiting information from previous calculations via Gaussian process regression. Predictive variance is used to assess whether a conformation is in the interpolation region, allowing a controlled trade-off between prediction accuracy and computational speed-up. For energies of relaxed conformations at the density functional level of theory (implicit solvent, DFT/BLYP-disp3/def2-TZVP), mean absolute errors of less than 1 kcal/mol were achieved. The study demonstrates that predictive machine learning models can be developed for structurally complex, pharmaceutically relevant compounds, potentially enabling considerable speed-ups in simulations of larger molecular structures. |
format | Online Article Text |
id | pubmed-3894151 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-38941512014-01-21 Machine Learning Estimates of Natural Product Conformational Energies Rupp, Matthias Bauer, Matthias R. Wilcken, Rainer Lange, Andreas Reutlinger, Michael Boeckler, Frank M. Schneider, Gisbert PLoS Comput Biol Research Article Machine learning has been used for estimation of potential energy surfaces to speed up molecular dynamics simulations of small systems. We demonstrate that this approach is feasible for significantly larger, structurally complex molecules, taking the natural product Archazolid A, a potent inhibitor of vacuolar-type ATPase, from the myxobacterium Archangium gephyra as an example. Our model estimates energies of new conformations by exploiting information from previous calculations via Gaussian process regression. Predictive variance is used to assess whether a conformation is in the interpolation region, allowing a controlled trade-off between prediction accuracy and computational speed-up. For energies of relaxed conformations at the density functional level of theory (implicit solvent, DFT/BLYP-disp3/def2-TZVP), mean absolute errors of less than 1 kcal/mol were achieved. The study demonstrates that predictive machine learning models can be developed for structurally complex, pharmaceutically relevant compounds, potentially enabling considerable speed-ups in simulations of larger molecular structures. Public Library of Science 2014-01-16 /pmc/articles/PMC3894151/ /pubmed/24453952 http://dx.doi.org/10.1371/journal.pcbi.1003400 Text en http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Rupp, Matthias Bauer, Matthias R. Wilcken, Rainer Lange, Andreas Reutlinger, Michael Boeckler, Frank M. Schneider, Gisbert Machine Learning Estimates of Natural Product Conformational Energies |
title | Machine Learning Estimates of Natural Product Conformational Energies |
title_full | Machine Learning Estimates of Natural Product Conformational Energies |
title_fullStr | Machine Learning Estimates of Natural Product Conformational Energies |
title_full_unstemmed | Machine Learning Estimates of Natural Product Conformational Energies |
title_short | Machine Learning Estimates of Natural Product Conformational Energies |
title_sort | machine learning estimates of natural product conformational energies |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3894151/ https://www.ncbi.nlm.nih.gov/pubmed/24453952 http://dx.doi.org/10.1371/journal.pcbi.1003400 |
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