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SuperPred: update on drug classification and target prediction
The SuperPred web server connects chemical similarity of drug-like compounds with molecular targets and the therapeutic approach based on the similar property principle. Since the first release of this server, the number of known compound–target interactions has increased from 7000 to 665 000, which...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4086135/ https://www.ncbi.nlm.nih.gov/pubmed/24878925 http://dx.doi.org/10.1093/nar/gku477 |
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author | Nickel, Janette Gohlke, Bjoern-Oliver Erehman, Jevgeni Banerjee, Priyanka Rong, Wen Wei Goede, Andrean Dunkel, Mathias Preissner, Robert |
author_facet | Nickel, Janette Gohlke, Bjoern-Oliver Erehman, Jevgeni Banerjee, Priyanka Rong, Wen Wei Goede, Andrean Dunkel, Mathias Preissner, Robert |
author_sort | Nickel, Janette |
collection | PubMed |
description | The SuperPred web server connects chemical similarity of drug-like compounds with molecular targets and the therapeutic approach based on the similar property principle. Since the first release of this server, the number of known compound–target interactions has increased from 7000 to 665 000, which allows not only a better prediction quality but also the estimation of a confidence. Apart from the addition of quantitative binding data and the statistical consideration of the similarity distribution in all drug classes, new approaches were implemented to improve the target prediction. The 3D similarity as well as the occurrence of fragments and the concordance of physico-chemical properties is also taken into account. In addition, the effect of different fingerprints on the prediction was examined. The retrospective prediction of a drug class (ATC code of the WHO) allows the evaluation of methods and descriptors for a well-characterized set of approved drugs. The prediction is improved by 7.5% to a total accuracy of 75.1%. For query compounds with sufficient structural similarity, the web server allows prognoses about the medical indication area of novel compounds and to find new leads for known targets. SuperPred is publicly available without registration at: http://prediction.charite.de. |
format | Online Article Text |
id | pubmed-4086135 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-40861352014-11-04 SuperPred: update on drug classification and target prediction Nickel, Janette Gohlke, Bjoern-Oliver Erehman, Jevgeni Banerjee, Priyanka Rong, Wen Wei Goede, Andrean Dunkel, Mathias Preissner, Robert Nucleic Acids Res Article The SuperPred web server connects chemical similarity of drug-like compounds with molecular targets and the therapeutic approach based on the similar property principle. Since the first release of this server, the number of known compound–target interactions has increased from 7000 to 665 000, which allows not only a better prediction quality but also the estimation of a confidence. Apart from the addition of quantitative binding data and the statistical consideration of the similarity distribution in all drug classes, new approaches were implemented to improve the target prediction. The 3D similarity as well as the occurrence of fragments and the concordance of physico-chemical properties is also taken into account. In addition, the effect of different fingerprints on the prediction was examined. The retrospective prediction of a drug class (ATC code of the WHO) allows the evaluation of methods and descriptors for a well-characterized set of approved drugs. The prediction is improved by 7.5% to a total accuracy of 75.1%. For query compounds with sufficient structural similarity, the web server allows prognoses about the medical indication area of novel compounds and to find new leads for known targets. SuperPred is publicly available without registration at: http://prediction.charite.de. Oxford University Press 2014-07-01 2014-05-30 /pmc/articles/PMC4086135/ /pubmed/24878925 http://dx.doi.org/10.1093/nar/gku477 Text en © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research. http://creativecommons.org/licenses/by/3.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Article Nickel, Janette Gohlke, Bjoern-Oliver Erehman, Jevgeni Banerjee, Priyanka Rong, Wen Wei Goede, Andrean Dunkel, Mathias Preissner, Robert SuperPred: update on drug classification and target prediction |
title | SuperPred: update on drug classification and target prediction |
title_full | SuperPred: update on drug classification and target prediction |
title_fullStr | SuperPred: update on drug classification and target prediction |
title_full_unstemmed | SuperPred: update on drug classification and target prediction |
title_short | SuperPred: update on drug classification and target prediction |
title_sort | superpred: update on drug classification and target prediction |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4086135/ https://www.ncbi.nlm.nih.gov/pubmed/24878925 http://dx.doi.org/10.1093/nar/gku477 |
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