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Machine Learning Estimates of Natural Product Conformational Energies
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Zeitschriftentitel: | PLoS Computational Biology |
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Personen und Körperschaften: | , , , , , , |
In: | PLoS Computational Biology, 10, 2014, 1, S. e1003400 |
Format: | E-Article |
Sprache: | Englisch |
veröffentlicht: |
Public Library of Science (PLoS)
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Schlagwörter: |
author_facet |
Rupp, Matthias Bauer, Matthias R. Wilcken, Rainer Lange, Andreas Reutlinger, Michael Boeckler, Frank M. Schneider, Gisbert Rupp, Matthias Bauer, Matthias R. Wilcken, Rainer Lange, Andreas Reutlinger, Michael Boeckler, Frank M. Schneider, Gisbert |
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author |
Rupp, Matthias Bauer, Matthias R. Wilcken, Rainer Lange, Andreas Reutlinger, Michael Boeckler, Frank M. Schneider, Gisbert |
spellingShingle |
Rupp, Matthias Bauer, Matthias R. Wilcken, Rainer Lange, Andreas Reutlinger, Michael Boeckler, Frank M. Schneider, Gisbert PLoS Computational Biology Machine Learning Estimates of Natural Product Conformational Energies Computational Theory and Mathematics Cellular and Molecular Neuroscience Genetics Molecular Biology Ecology Modeling and Simulation Ecology, Evolution, Behavior and Systematics |
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rupp, matthias |
spelling |
Rupp, Matthias Bauer, Matthias R. Wilcken, Rainer Lange, Andreas Reutlinger, Michael Boeckler, Frank M. Schneider, Gisbert 1553-7358 Public Library of Science (PLoS) Computational Theory and Mathematics Cellular and Molecular Neuroscience Genetics Molecular Biology Ecology Modeling and Simulation Ecology, Evolution, Behavior and Systematics http://dx.doi.org/10.1371/journal.pcbi.1003400 Machine Learning Estimates of Natural Product Conformational Energies PLoS Computational Biology |
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title |
Machine Learning Estimates of Natural Product Conformational Energies |
title_unstemmed |
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 |
Computational Theory and Mathematics Cellular and Molecular Neuroscience Genetics Molecular Biology Ecology Modeling and Simulation Ecology, Evolution, Behavior and Systematics |
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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 |
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spelling | Rupp, Matthias Bauer, Matthias R. Wilcken, Rainer Lange, Andreas Reutlinger, Michael Boeckler, Frank M. Schneider, Gisbert 1553-7358 Public Library of Science (PLoS) Computational Theory and Mathematics Cellular and Molecular Neuroscience Genetics Molecular Biology Ecology Modeling and Simulation Ecology, Evolution, Behavior and Systematics http://dx.doi.org/10.1371/journal.pcbi.1003400 Machine Learning Estimates of Natural Product Conformational Energies PLoS Computational Biology |
spellingShingle | Rupp, Matthias, Bauer, Matthias R., Wilcken, Rainer, Lange, Andreas, Reutlinger, Michael, Boeckler, Frank M., Schneider, Gisbert, PLoS Computational Biology, Machine Learning Estimates of Natural Product Conformational Energies, Computational Theory and Mathematics, Cellular and Molecular Neuroscience, Genetics, Molecular Biology, Ecology, Modeling and Simulation, Ecology, Evolution, Behavior and Systematics |
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 |
title_unstemmed | Machine Learning Estimates of Natural Product Conformational Energies |
topic | Computational Theory and Mathematics, Cellular and Molecular Neuroscience, Genetics, Molecular Biology, Ecology, Modeling and Simulation, Ecology, Evolution, Behavior and Systematics |
url | http://dx.doi.org/10.1371/journal.pcbi.1003400 |