Prediction of drug target groups based on chemical-chemical similarities and chemical-chemical/protein connections.

Drug-target interaction is a key research topic in drug discovery since correct identification of target proteins of drug candidates can help screen out those with unacceptable toxicities, thereby saving expense. In this study, we developed a novel computational approach to predict drug target groups that may reduce the number of candidate target proteins associated with a query drug. A benchmark dataset, consisting of 3028 drugs assigned within nine categories, was constructed by collecting data from KEGG. The nine categories are (1) G protein-coupled receptors, (2) cytokine receptors, (3) nuclear receptors, (4) ion channels, (5) transporters, (6) enzymes, (7) protein kinases, (8) cellular antigens and (9) pathogens. The proposed method combines the data gleaned from chemical-chemical similarities, chemical-chemical connections and chemical-protein connections to allocate drugs to each of the nine target groups. A jackknife test applied to the training dataset that was constructed from the benchmark dataset, provided an overall correct prediction rate of 87.45%, as compared to 87.79% for the test dataset that was constructed by randomly selecting 10% of samples from the benchmark dataset. These prediction rates are much higher than the 11.11% achieved by random guesswork. These promising results suggest that the proposed method can become a useful tool in identifying drug target groups. This article is part of a Special Issue entitled: Computational Proteomics, Systems Biology & Clinical Implications. Guest Editor: Yudong Cai.

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