Drug-target interaction is an important topic in drug discovery and drug repositioning. KEGG database offers a
drug annotation and classification using a target-based classification system. In this study, we gave an investigation on
five target-based classes: (I) G protein-coupled receptors; (II) Nuclear receptors; (III) Ion channels; (IV) Enzymes; (V)
Pathogens, using molecular descriptors to represent each drug compound. Two popular feature selection methods,
maximum relevance minimum redundancy and incremental feature selection, were adopted to extract the important
descriptors. Meanwhile, an optimal prediction model based on nearest neighbor algorithm was constructed, which got the
best result in identifying drug target-based classes. Finally, some key descriptors were discussed to uncover their
important roles in the identification of drug-target classes.
Keywords: Drug-target interaction, molecular descriptors, maximum relevance minimum redundancy, incremental feature
selection, nearest neighbor algorithm.
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