QSAR and classification study of 1,4-dihydropyridine calcium channel antagonists based on least squares support vector machines

Xiaojun Yao, Huanxiang Liu, Ruisheng Zhang, Mancang Liu, Zhide Hu, A. Panaye, J. P. Doucet, Botao Fan

Research output: Contribution to journalArticlepeer-review

63 Citations (Scopus)

Abstract

The least squares support vector machine (LSSVM), as a novel machine learning algorithm, was used to develop quantitative and classification models as a potential screening mechanism for a novel series of 1,4-dihydropyridine calcium channel antagonists for the first time. Each compound was represented by calculated structural descriptors that encode constitutional, topological, geometrical, electrostatic, quantum-chemical features. The heuristic method was then used to search the descriptor space and select the descriptors responsible for activity. Quantitative modeling results in a nonlinear, seven-descriptor model based on LSSVM with mean-square errors 0.2593, a predicted correlation coefficient (R2) 0.8696, and a cross-validated correlation coefficient (Rcv2) 0.8167. The best classification results are found using LSSVM: the percentage (%) of correct prediction based on leave one out cross-validation was 91.1%. This paper provides a new and effective method for drug design and screening.

Original languageEnglish
Pages (from-to)348-356
Number of pages9
JournalMolecular Pharmaceutics
Volume2
Issue number5
DOIs
Publication statusPublished - Sept 2005
Externally publishedYes

Keywords

  • Calcium channel antagonists
  • Least squares support vector machines
  • QSAR

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