APPLICATION OF MULTIVARIATE IMAGE ANALYSIS IN QSPR STUDY OF pKa OF VARIOUS ACIDS BY PRINCIPAL COMPONENTS-LEAST SQUARES SUPPORT VECTOR MACHINE
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...
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| Autores principales: | , , , |
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| Lenguaje: | English |
| Publicado: |
Sociedad Chilena de Química
2015
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| Materias: | |
| Acceso en línea: | http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0717-97072015000300001 |
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| Sumario: | 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. |
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