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Drug-target Interaction Prediction from Chemical, Genomic and Pharmacological Data in an Integrated Framework by Yoshihiro Yamanishi; Masaaki Kotera; Minoru Kanehisa; Susumu Goto is a Biochemistry, Genetics and Molecular Biology article available to read on EtoBox.

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## Abstract Motivation: In silico prediction of drug–target interactions from heterogeneous biological data is critical in the search for drugs and therapeutic targets for known diseases such as cancers. There is therefore a strong incentive to develop new methods capable of detecting these potential drug–target interactions efficiently. Results: In this article, we investigate the relationship between the chemical space, the pharmacological space and the topology of drug–target interaction networks, and show that drug–target interactions are more correlated with pharmacological effect similarity than with chemical structure similarity. We then develop a new method to predict unknown drug–target interactions from chemical, genomic and pharmacological data on a large scale. The proposed method consists of two steps: (i) prediction of pharmacological effects from chemical structures of given compounds and (ii) inference of unknown drug–target interactions based on the pharmacological effect similarity in the framework of supervised bipartite graph inference. The originality of the proposed method lies in the prediction of potential pharmacological similarity for any drug candidate c

Who reads Drug-target Interaction Prediction from Chemical, Genomic and Pharmacological Data in an Integrated Framework?

Author

Yoshihiro Yamanishi; Masaaki Kotera; Minoru Kanehisa; Susumu Goto

Publisher

Oxford University Press; Oxford University Press (OUP) (ISSN 1367-4803)

Published

2010

Language

EN

Field

Biochemistry, Genetics and Molecular Biology (Life Sciences)