Application of multivariate linear regression and artificial neural networks to predict the antimicrobial activity of some anilide derivatives by quantitative structure-activity relationship (QSAR) method
Subject Areas :
mehdi
nekoei
1
(Islamic Azad University of shahrood branch)
Parastoo
pourali
2
(IAU Shahrood Branch)
Syed Hamed
Mppsavi
3
(IAU Shahrood Branch)
Keywords: multiple linear regression, Artificial Neural Network, Aniline derivatives, Quantitative structure-activity relationship,
Abstract :
Quantitative structure-activity relationship (QSAR) study was performed to predict the antimicrobial activity of some anilide derivatives using multivariate linear regression (MLR) and artificial neural networks (ANN). First, the structure of the compounds, the drawing and the appropriate group of descriptors were calculated. Then, the step selection method was used to obtain the best descriptors that were most associated with the antimicrobial activity of the compounds. With this method, 5 descriptors were selected and first the linear MLR model was constructed. Then, artificial neural network was used to obtain better results. The values of coefficient of determination (R2) and root mean square error (RMSE) for the test series were 0.07 and 0.073 for the MLR linear model and 0.613 and 0.021 for the nonlinear ANN model, respectively. Statistical data show the superiority of ANN method over MLR method.
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