期刊
ANALYTICA CHIMICA ACTA
卷 909, 期 -, 页码 41-50出版社
ELSEVIER
DOI: 10.1016/j.aca.2016.01.014
关键词
Drug-target interactions; Regularized least squares; Kernel fusion; PubChem BioAssay; Drug repositioning
资金
- Intramural Research Program of the NIH, National Library of Medicine
Identification of drug-target interactions (DTI) is a central task in drug discovery processes. In this work, a simple but effective regularized least squares integrating with nonlinear kernel fusion (RLS-KF) algorithm is proposed to perform DTI predictions. Using benchmark DTI datasets, our proposed algorithm achieves the state-of-the-art results with area under precision-recall curve (AUPR) of 0.915, 0.925, 0.853 and 0.909 for enzymes, ion channels (IC), G protein-coupled receptors (GPCR) and nuclear receptors (NR) based on 10 fold cross-validation. The performance can further be improved by using a recalculated kernel matrix, especially for the small set of nuclear receptors with AUPR of 0.945. Importantly, most of the top ranked interaction predictions can be validated by experimental data reported in the literature, bioassay results in the PubChem BioAssay database, as well as other previous studies. Our analysis suggests that the proposed RLS-KF is helpful for studying DTI, drug repositioning as well as poly-pharmacology, and may help to accelerate drug discovery by identifying novel drug targets. Published by Elsevier B.V.
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