{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T21:24:50Z","timestamp":1775942690088,"version":"3.50.1"},"reference-count":38,"publisher":"Oxford University Press (OUP)","issue":"21","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2011,11,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Motivation: The in silico prediction of potential interactions between drugs and target proteins is of core importance for the identification of new drugs or novel targets for existing drugs. However, only a tiny portion of all drug\u2013target pairs in current datasets are experimentally validated interactions. This motivates the need for developing computational methods that predict true interaction pairs with high accuracy.<\/jats:p>\n               <jats:p>Results: We show that a simple machine learning method that uses the drug\u2013target network as the only source of information is capable of predicting true interaction pairs with high accuracy. Specifically, we introduce interaction profiles of drugs (and of targets) in a network, which are binary vectors specifying the presence or absence of interaction with every target (drug) in that network. We define a kernel on these profiles, called the Gaussian Interaction Profile (GIP) kernel, and use a simple classifier, (kernel) Regularized Least Squares (RLS), for prediction drug\u2013target interactions. We test comparatively the effectiveness of RLS with the GIP kernel on four drug\u2013target interaction networks used in previous studies. The proposed algorithm achieves area under the precision\u2013recall curve (AUPR) up to 92.7, significantly improving over results of state-of-the-art methods. Moreover, we show that using also kernels based on chemical and genomic information further increases accuracy, with a neat improvement on small datasets. These results substantiate the relevance of the network topology (in the form of interaction profiles) as source of information for predicting drug\u2013target interactions.<\/jats:p>\n               <jats:p>Availability: Software and Supplementary Material are available at http:\/\/cs.ru.nl\/~tvanlaarhoven\/drugtarget2011\/.<\/jats:p>\n               <jats:p>Contact: \u00a0tvanlaarhoven@cs.ru.nl; elenam@cs.ru.nl<\/jats:p>\n               <jats:p>Supplementary Information: \u00a0Supplementary data are available at Bioinformatics online.<\/jats:p>","DOI":"10.1093\/bioinformatics\/btr500","type":"journal-article","created":{"date-parts":[[2011,9,6]],"date-time":"2011-09-06T00:26:54Z","timestamp":1315268814000},"page":"3036-3043","source":"Crossref","is-referenced-by-count":836,"title":["Gaussian interaction profile kernels for predicting drug\u2013target interaction"],"prefix":"10.1093","volume":"27","author":[{"given":"Twan","family":"van Laarhoven","sequence":"first","affiliation":[{"name":"1 Department of Computer Science, Radboud University Nijmegen and 2Computational Drug Discovery, Center for Molecular and Biomolecular Informatics, Radboud University Nijmegen Medical Center, Nijmegen, The Netherlands"}]},{"given":"Sander B.","family":"Nabuurs","sequence":"additional","affiliation":[{"name":"1 Department of Computer Science, Radboud University Nijmegen and 2Computational Drug Discovery, Center for Molecular and Biomolecular Informatics, Radboud University Nijmegen Medical Center, Nijmegen, The Netherlands"}]},{"given":"Elena","family":"Marchiori","sequence":"additional","affiliation":[{"name":"1 Department of Computer Science, Radboud University Nijmegen and 2Computational Drug Discovery, Center for Molecular and Biomolecular Informatics, Radboud University Nijmegen Medical Center, Nijmegen, The Netherlands"}]}],"member":"286","published-online":{"date-parts":[[2011,9,4]]},"reference":[{"key":"2023012511341713700_B1","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1145\/1015330.1015394","article-title":"Unifying collaborative and content-based filtering","volume-title":"ICML '04: Proceedings of the 21st International Conference on Machine learning.","author":"Basilico","year":"2004"},{"issue":"Suppl. 1","key":"2023012511341713700_B2","doi-asserted-by":"crossref","first-page":"i38","DOI":"10.1093\/bioinformatics\/bti1016","article-title":"Kernel methods for predicting protein\u2013protein interactions","volume":"21","author":"Ben-Hur","year":"2005","journal-title":"Bioinformatics"},{"key":"2023012511341713700_B3","doi-asserted-by":"crossref","first-page":"2397","DOI":"10.1093\/bioinformatics\/btp433","article-title":"Supervised prediction of drug-target interactions using bipartite local models","volume":"25","author":"Bleakley","year":"2009","journal-title":"Bioinformatics"},{"key":"2023012511341713700_B4","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1126\/science.1158140","article-title":"Drug target identification using side-effect similarity","volume":"321","author":"Campillos","year":"2008","journal-title":"Science"},{"key":"2023012511341713700_B5","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1038\/nbt1273","article-title":"Structure-based maximal affinity model predicts small-molecule druggability","volume":"25","author":"Cheng","year":"2007","journal-title":"Nat. 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