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dc.contributor.author
Schneider, Gisbert
dc.contributor.author
Rupp, Matthias
dc.contributor.author
Bauer, Matthias R.
dc.contributor.author
Wilcken, Rainer
dc.contributor.author
Lange, Andreas
dc.contributor.author
Reutlinger, Michael
dc.contributor.author
Boeckler, Frank M.
dc.date.accessioned
2019-01-17T11:51:52Z
dc.date.available
2017-06-11T10:55:32Z
dc.date.available
2019-01-17T11:51:52Z
dc.date.issued
2014-01-16
dc.identifier.issn
1553-734X
dc.identifier.issn
1553-7358
dc.identifier.other
10.1371/journal.pcbi.1003400
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/86677
dc.identifier.doi
10.3929/ethz-b-000086677
dc.description.abstract
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.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
Public Library of Science
en_US
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
dc.title
Machine Learning Estimates of Natural Product Conformational Energies
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 4.0 International
ethz.journal.title
PLoS Computational Biology
ethz.journal.volume
10
en_US
ethz.journal.issue
1
en_US
ethz.journal.abbreviated
PLOS comput. biol.
ethz.pages.start
e1003400
en_US
ethz.size
8 p.
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.identifier.wos
ethz.identifier.nebis
005410277
ethz.publication.place
San Francisco, CA, USA
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02020 - Dep. Chemie und Angewandte Biowiss. / Dep. of Chemistry and Applied Biosc.::02534 - Institut für Pharmazeutische Wiss. / Institute of Pharmaceutical Sciences::03852 - Schneider, Gisbert / Schneider, Gisbert
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02020 - Dep. Chemie und Angewandte Biowiss. / Dep. of Chemistry and Applied Biosc.::02534 - Institut für Pharmazeutische Wiss. / Institute of Pharmaceutical Sciences::03852 - Schneider, Gisbert / Schneider, Gisbert
ethz.date.deposited
2017-06-11T10:59:48Z
ethz.source
ECIT
ethz.identifier.importid
imp59365218c99c662348
ethz.ecitpid
pub:136453
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2017-07-15T15:43:12Z
ethz.rosetta.lastUpdated
2019-01-17T11:52:11Z
ethz.rosetta.exportRequired
false
ethz.rosetta.versionExported
true
ethz.COinS
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