Quantitative structure - Activity relationship study on a series of novel ligands binding to central benzodiazepine receptor by using the combination of heuristic method and support vector machines

Shen Qin, Huanxiang Liu, Jie Wang, Xiaojun Yao, Mancang Liu, Zhide Hu, Botao Fan

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)

Abstract

In this study, Heuristic Method (HM) and Support Vector Machines (SVMs) were used for Quantitative Structure - Activity Relationship (QSAR) study of a series of 2-aryl (heteroaryl)-2,5-dihydropyrazolo [4,3-c] quinolin-3-(3H)-ones, which have high affinity with central Benzodiazepine Receptor (BzR). Seven molecular descriptors selected by the HM in CODESSA were used as inputs for SVM. The prediction results are in good agreement with the experimental values. The correlation coefficients R2 of the nonlinear SVM model were 0.93 and 0.96 for the training and testing sets, respectively. This paper proposes a new and effective method to design new ligands of BzR based on QSAR study.

Original languageEnglish
Pages (from-to)443-451
Number of pages9
JournalQSAR and Combinatorial Science
Volume26
Issue number4
DOIs
Publication statusPublished - Apr 2007
Externally publishedYes

Keywords

  • Benzodiazepine receptor
  • QSAR
  • Support vector machines

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