Quantum Chemical QSAR Models to Distinguish Between Inhibitory Activities of Sulfonamides Against Human Carbonic Anhydrases I and II and Bovine IV Isozymes

Omar Deeb, Mohammad Goodarzi, Padmaker V. Khadikar

Research output: Contribution to journalArticle

5 Scopus citations

Abstract

Linear and nonlinear quantitative structure activity relationship models for predicting the inhibitory activities of sulfonamides toward different carbonic anhydrase isozymes were developed based on multilinear regression, principal component-artificial neural network and correlation ranking-principal component analysis, to identify a set of structurally based numerical descriptors. Multilinear regression was used to build linear quantitative structure activity relationship models using 53 compounds with their quantum chemical descriptors. For each type of isozyme, separate quantitative structure activity relationship models were obtained. It was found that the hydration energy plays a significant role in the binding of ligands to the CAI isozyme, whereas the presence of five-membered ring was detected as a major factor for the binding to the CAII isozyme. It was also found that the softness exhibited significant effect on the binding to CAIV isozyme. Principal component-artificial neural network and correlation ranking-principal component analysis analyses provide models with better prediction capability for the three types of the carbonic anhydrase isozyme inhibitory activity than those obtained by multilinear regression analysis. The best models, with improved prediction capability, were obtained for the hCAII isozyme activity. Models predictivity was evaluated by cross-validation, using an external test set and chance correlation test.

Original languageEnglish (US)
Pages (from-to)514-522
Number of pages9
JournalChemical Biology and Drug Design
Volume79
Issue number4
DOIs
StatePublished - Apr 2012

Keywords

  • Carbonic anhydrase isozymes and inhibitors
  • Correlation ranking-principal component analysis
  • Principal component-artificial neural network
  • Quantitative structure activity relationship
  • Quantum chemical descriptors

ASJC Scopus subject areas

  • Biochemistry
  • Molecular Medicine
  • Pharmacology
  • Drug Discovery
  • Organic Chemistry

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