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Predicting Drug-Target Interactions for New Drug Compounds Using a Weighted Nearest Neighbor Profile
In silico discovery of interactions between drug compounds and target proteins is of core importance for improving the efficiency of the laborious and costly experimental determination of drug-target interaction. Drug-target interaction data are available for many classes of pharmaceutically useful...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3694117/ https://www.ncbi.nlm.nih.gov/pubmed/23840562 http://dx.doi.org/10.1371/journal.pone.0066952 |
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author | van Laarhoven, Twan Marchiori, Elena |
author_facet | van Laarhoven, Twan Marchiori, Elena |
author_sort | van Laarhoven, Twan |
collection | PubMed |
description | In silico discovery of interactions between drug compounds and target proteins is of core importance for improving the efficiency of the laborious and costly experimental determination of drug-target interaction. Drug-target interaction data are available for many classes of pharmaceutically useful target proteins including enzymes, ion channels, GPCRs and nuclear receptors. However, current drug-target interaction databases contain a small number of drug-target pairs which are experimentally validated interactions. In particular, for some drug compounds (or targets) there is no available interaction. This motivates the need for developing methods that predict interacting pairs with high accuracy also for these 'new' drug compounds (or targets). We show that a simple weighted nearest neighbor procedure is highly effective for this task. We integrate this procedure into a recent machine learning method for drug-target interaction we developed in previous work. Results of experiments indicate that the resulting method predicts true interactions with high accuracy also for new drug compounds and achieves results comparable or better than those of recent state-of-the-art algorithms. Software is publicly available at http://cs.ru.nl/~tvanlaarhoven/drugtarget2013/. |
format | Online Article Text |
id | pubmed-3694117 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-36941172013-07-09 Predicting Drug-Target Interactions for New Drug Compounds Using a Weighted Nearest Neighbor Profile van Laarhoven, Twan Marchiori, Elena PLoS One Research Article In silico discovery of interactions between drug compounds and target proteins is of core importance for improving the efficiency of the laborious and costly experimental determination of drug-target interaction. Drug-target interaction data are available for many classes of pharmaceutically useful target proteins including enzymes, ion channels, GPCRs and nuclear receptors. However, current drug-target interaction databases contain a small number of drug-target pairs which are experimentally validated interactions. In particular, for some drug compounds (or targets) there is no available interaction. This motivates the need for developing methods that predict interacting pairs with high accuracy also for these 'new' drug compounds (or targets). We show that a simple weighted nearest neighbor procedure is highly effective for this task. We integrate this procedure into a recent machine learning method for drug-target interaction we developed in previous work. Results of experiments indicate that the resulting method predicts true interactions with high accuracy also for new drug compounds and achieves results comparable or better than those of recent state-of-the-art algorithms. Software is publicly available at http://cs.ru.nl/~tvanlaarhoven/drugtarget2013/. Public Library of Science 2013-06-26 /pmc/articles/PMC3694117/ /pubmed/23840562 http://dx.doi.org/10.1371/journal.pone.0066952 Text en © 2013 van Laarhoven, Marchiori http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article van Laarhoven, Twan Marchiori, Elena Predicting Drug-Target Interactions for New Drug Compounds Using a Weighted Nearest Neighbor Profile |
title | Predicting Drug-Target Interactions for New Drug Compounds Using a Weighted Nearest Neighbor Profile |
title_full | Predicting Drug-Target Interactions for New Drug Compounds Using a Weighted Nearest Neighbor Profile |
title_fullStr | Predicting Drug-Target Interactions for New Drug Compounds Using a Weighted Nearest Neighbor Profile |
title_full_unstemmed | Predicting Drug-Target Interactions for New Drug Compounds Using a Weighted Nearest Neighbor Profile |
title_short | Predicting Drug-Target Interactions for New Drug Compounds Using a Weighted Nearest Neighbor Profile |
title_sort | predicting drug-target interactions for new drug compounds using a weighted nearest neighbor profile |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3694117/ https://www.ncbi.nlm.nih.gov/pubmed/23840562 http://dx.doi.org/10.1371/journal.pone.0066952 |
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