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- W2147629497 abstract "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." @default.
- W2147629497 created "2016-06-24" @default.
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- W2147629497 date "2010-11-01" @default.
- W2147629497 modified "2023-10-18" @default.
- W2147629497 title "Using Compound Similarity and Functional Domain Composition for Prediction of Drug-Target Interaction Networks" @default.
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- W2147629497 doi "https://doi.org/10.2174/157340610793563983" @default.
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