Analysis of A Drug Target-based Classification System using Molecular Descriptors

Title:Analysis of A Drug Target-based Classification System using Molecular Descriptors

Volume: 19 Issue: 2

Author(s): Jing Lu, Pin Zhang, Yi Bi and Xiaomin Luo

Affiliation:

Keywords: Drug-target interaction, molecular descriptors, maximum relevance minimum redundancy, incremental feature selection, nearest neighbor algorithm.

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.

Cite this article as:

Lu Jing, Zhang Pin, Bi Yi and Luo Xiaomin, Analysis of A Drug Target-based Classification System using Molecular Descriptors, Combinatorial Chemistry & High Throughput Screening 2016; 19 (2) . https://dx.doi.org/10.2174/1386207319666151110122335