Application of support vector machine in QSAR study of triazolyl thiophenes as cyclin dependent kinase-5 inhibitors for their anti-alzheimer activity

Abstract

<span style="font-size:11.0pt;font-family: " times="" new="" roman";mso-fareast-font-family:"times="" roman";mso-bidi-font-family:="" mangal;mso-ansi-language:en-gb;mso-fareast-language:en-us;mso-bidi-language:="" hi"="" lang="EN-GB">Quantitative structure-activity relationship (QSAR) models are mathematical equations constructing a relationship between chemical structures and biological activities. A series of triazolyl thiophenes as cyclin dependent kinase 5 (cdk5/p25) inhibitors have been selected to establish QSAR models. In this work some chemometrics methods are applied for modeling and prediction of the anti-Alzheimer activity of these compounds using descriptors that are calculated from the molecular structures. First, stepwise multiple linear regression method (MLR) is used to select descriptors which are responsible for the anti-Alzheimer activity of these compounds. Then support vector regression (SVR) and partial least squares (PLS) are utilized to construct the nonlinear and linear quantitative structure–activity relationship models. Results demonstrate that the SVR model offers powerful prediction capabilities.</span>