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Comparative Evaluation of Multiple Linear Regression and Support vector Machine aided Linear and Non-linear QSAR Models

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Author Affiliations

  • 1Govt. Shaheed Bhagirath Silawat College, Depalpur, Indore, MP, INDIA
  • 2Dept. of Chemistry, Govt. Holkar Science College, Indore, MP, INDIA
  • 3Dept. of Pharmaceutical Chemistry, Softvision College, Indore, MP, INDIA

Res.J.chem.sci., Volume 4, Issue (7), Pages 24-29, July,18 (2014)

Abstract

Type 2 diabetes still remains a major challenge to human health management. Protein tyrosine phosphate 1B has been continuously explored for its therapeutic potential to treat type 2 diabetes as it is linked with negative regulation of insusignal transduction. QSAR studies were performed on derivatives of 2and SVM aided linear and non-linear models were obtained which were further evaluated to identify descriptors revealing underlying structure-activity relationship. QSAR models were validated through a series of validation techniques like Yrandomization and descriptor sensitivity in addition to internal validation parameters. Information content index (IC1) of neighbourhood symmetry of order-1 has beeactivity relationship of 2-arylsulphonylaminobenzothiazoles derivatives. Geary autopolarizability are also actively correlated to biological response of tyrosine phosphate 1B inhibitors

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