title: Analysis of A Drug Target-based Classification System using Molecular Descriptors.
abstract: 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.
authors.affiliation: School of Pharmacy, Yantai University, Yantai, Shandong 264005, China. lujing_ytu@126.com.
authors.forename: Jing
authors.initials: J
authors.lastname: Lu
authors.forename: Pin
authors.initials: P
authors.lastname: Zhang
authors.forename: Yi
authors.initials: Y
authors.lastname: Bi
authors.forename: Xiaomin
authors.initials: X
authors.lastname: Luo
mesh_terms: D000465:Algorithms; D062126:Databases, Chemical; D004798:Enzymes / Q000737:chemistry; D057166:High-Throughput Screening Assays; D007473:Ion Channels / Q000737:chemistry; D008829:Microbiology; D058990:Molecular Targeted Therapy / Q000145:classification*; D004364:Pharmaceutical Preparations / Q000737:chemistry* / Q000145:classification*; D011789:Quantum Theory; D018160:Receptors, Cytoplasmic and Nuclear / Q000737:chemistry; D043562:Receptors, G-Protein-Coupled / Q000737:chemistry; D055672:Static Electricity
chemical_list: D004798:Enzymes; D007473:Ion Channels; D004364:Pharmaceutical Preparations; D018160:Receptors, Cytoplasmic and Nuclear; D043562:Receptors, G-Protein-Coupled