An eigenvalue transformation technique for predicting drug-target interaction

Scientific Reports
Qifan KuangMenglong Li

Abstract

The prediction of drug-target interactions is a key step in the drug discovery process, which serves to identify new drugs or novel targets for existing drugs. However, experimental methods for predicting drug-target interactions are expensive and time-consuming. Therefore, the in silico prediction of drug-target interactions has recently attracted increasing attention. In this study, we propose an eigenvalue transformation technique and apply this technique to two representative algorithms, the Regularized Least Squares classifier (RLS) and the semi-supervised link prediction classifier (SLP), that have been used to predict drug-target interaction. The results of computational experiments with these techniques show that algorithms including eigenvalue transformation achieved better performance on drug-target interaction prediction than did the original algorithms. These findings show that eigenvalue transformation is an efficient technique for improving the performance of methods for predicting drug-target interactions. We further show that, in theory, eigenvalue transformation can be viewed as a feature transformation on the kernel matrix. Accordingly, although we only apply this technique to two algorithms in the current stu...Continue Reading

References

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Citations

Jul 6, 2017·Molecules : a Journal of Synthetic Chemistry and Natural Product Chemistry·Fan-Rong MengJi-Yong An
Aug 23, 2019·Molecules : a Journal of Synthetic Chemistry and Natural Product Chemistry·Yang LiZheng Wang
Jan 18, 2020·Briefings in Bioinformatics·Maryam BagherianKayvan Najarian
Apr 11, 2019·Protein and Peptide Letters·Yijie DingFei Guo
Jun 4, 2017·The AAPS Journal·Tiejun ChengYanli Wang
Aug 9, 2019·Current Medicinal Chemistry·Chen Wang, Lukasz Kurgan
Jul 2, 2019·Journal of Chemical Information and Modeling·Liang-Yong XiaYong Liang
Nov 4, 2021·Interdisciplinary Sciences, Computational Life Sciences·Yan ZhangBin Yu

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Software Mentioned

ChEMBL
SuperTarget
RLS
KP
SIMCOMP
Kegg
SLP

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