Machine Learning Estimates of Natural Product Conformational Energies

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RUPP, Matthias, Matthias R. BAUER, Rainer WILCKEN, Andreas LANGE, Michael REUTLINGER, Frank M. BOECKLER, Gisbert SCHNEIDER, 2014. Machine Learning Estimates of Natural Product Conformational Energies. In: PLoS Computational Biology. Public Library of Science (PLoS). 10(1), e1003400. ISSN 1553-734X. eISSN 1553-7358. Available under: doi: 10.1371/journal.pcbi.1003400

@article{Rupp2014-01Machi-52772, title={Machine Learning Estimates of Natural Product Conformational Energies}, year={2014}, doi={10.1371/journal.pcbi.1003400}, number={1}, volume={10}, issn={1553-734X}, journal={PLoS Computational Biology}, author={Rupp, Matthias and Bauer, Matthias R. and Wilcken, Rainer and Lange, Andreas and Reutlinger, Michael and Boeckler, Frank M. and Schneider, Gisbert}, note={Article Number: e1003400} }

<rdf:RDF xmlns:dcterms="" xmlns:dc="" xmlns:rdf="" xmlns:bibo="" xmlns:dspace="" xmlns:foaf="" xmlns:void="" xmlns:xsd="" > <rdf:Description rdf:about=""> <dc:creator>Schneider, Gisbert</dc:creator> <dc:language>eng</dc:language> <dc:contributor>Schneider, Gisbert</dc:contributor> <dc:creator>Reutlinger, Michael</dc:creator> <dc:contributor>Lange, Andreas</dc:contributor> <dc:creator>Boeckler, Frank M.</dc:creator> <dcterms:hasPart rdf:resource=""/> <dc:contributor>Rupp, Matthias</dc:contributor> <foaf:homepage rdf:resource="http://localhost:8080/jspui"/> <dc:date rdf:datatype="">2021-02-09T09:15:40Z</dc:date> <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/> <dc:rights>Attribution 4.0 International</dc:rights> <dspace:isPartOfCollection rdf:resource=""/> <dcterms:issued>2014-01</dcterms:issued> <bibo:uri rdf:resource=""/> <dcterms:title>Machine Learning Estimates of Natural Product Conformational Energies</dcterms:title> <dspace:hasBitstream rdf:resource=""/> <dc:creator>Bauer, Matthias R.</dc:creator> <dcterms:abstract xml:lang="eng">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.</dcterms:abstract> <dcterms:isPartOf rdf:resource=""/> <dcterms:available rdf:datatype="">2021-02-09T09:15:40Z</dcterms:available> <dc:contributor>Wilcken, Rainer</dc:contributor> <dc:creator>Wilcken, Rainer</dc:creator> <dc:contributor>Reutlinger, Michael</dc:contributor> <dc:contributor>Boeckler, Frank M.</dc:contributor> <dc:creator>Rupp, Matthias</dc:creator> <dc:contributor>Bauer, Matthias R.</dc:contributor> <dc:creator>Lange, Andreas</dc:creator> <dcterms:rights rdf:resource=""/> </rdf:Description> </rdf:RDF>

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