Using Compound Similarity and Functional Domain Composition for Prediction of Drug-Target Interaction Networks

Author(s): Lei Chen, Zhi-Song He, Tao Huang, Yu-Dong Cai.

Journal Name: Medicinal Chemistry

Volume 6 , Issue 6 , 2010


Study of interactions between drugs and target proteins is an essential step in genomic drug discovery. It is very hard to determine the compound-protein interactions or drug-target interactions by experiment alone. As supplementary, effective prediction model using machine learning or data mining methods can provide much help. In this study, a prediction method based on Nearest Neighbor Algorithm and a novel metric, which was obtained by combining compound similarity and functional domain composition, was proposed. The target proteins were divided into the following groups: enzymes, ion channels, G protein-coupled receptors, and nuclear receptors. As a result, four predictors with the optimal parameters were established. The overall prediction accuracies, evaluated by jackknife cross-validation test, for four groups of target proteins are 90.23%, 94.74%, 97.80%, and 97.51%, respectively, indicating that compound similarity and functional domain composition are very effective to predict drug-target interaction networks.

Keywords: Compound similarity, drug-target interaction network, functional domain composition, jackknife cross-validation test, Matthew's correlation coefficient, nearest neighbor algorithm, SMILES, MACC, SBASE-A

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Article Details

Year: 2010
Page: [388 - 395]
Pages: 8
DOI: 10.2174/157340610793563983
Price: $58

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