Application of multivariate image analysis in qspr study of pka of various acids by principal components-least squares support vector machine

• SOMAYEH VEYSEH ^[1] ; HAMIDEH HAMZEHALI ^[1] ; ALI NIAZI ^[1] ; JAHAN B GHASEMI ^[2]
  1. [1] Islamic Azad University Arak Branch Faculty of Science
  2. [2] K.N. Toosi University of Technology Faculty of Science Department of Chemistry
• Localización: Journal of the Chilean Chemical Society (Boletín de la Sociedad Chilena de Química), ISSN-e 0717-6309, ISSN 0366-1644, Vol. 60, Nº. 3, 2015, págs. 2985-2987
• Idioma: inglés
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• Resumen

  • A new implemented quantitative structure-property relationships (QSPR) method, whose descriptors achieved from bidimensional images, was suggested for the predicting of acidity constant (pKa) of various acid. The resulted descriptors were subjected to principal component analysis (PCA) and the most significant principal components (PCs) were extracted. Multivariate image analysis applied to QSPR modeling was done by means of principal component-least squares support vector machine (PC-LSSVM) methods. The resulted model showed high prediction ability with root mean square error of prediction of 0.0195 for PC-LSSVM.