Prediction of antibacterial activity of pleuromutilin derivatives by genetic algorithm–multiple linear regression (GA–MLR)

Abstract

Use of quantitative structure-activity relation- ships for prediction of the antibacterial activity of pleuromutilin derivatives was studied. A suitable set of molecular descriptors was calculated and the important descriptors were selected by using the variable selections of stepwise multiple linear regression and genetic algo- rithm. Principal-components analysis was used to select the training set. The models were validated by use of leave- one-out (LOO) cross-validation, external test set, and the Y-randomization test. Comparison of the results obtained revealed the superiority of the genetic algorithm over the stepwise multiple regression method for feature selection. One genetic algorithm-multiple linear regression (GA- MLR) model with six selected descriptors was obtained. The root mean square errors of the training and test sets for the GA-MLR model were calculated to be 0.423 and 0.523, and the correlation coefficients were 0.839 and 0.807. The statistical parameter of LOO cross validation test correlation coefficients on the GA-MLR model was 0.760. The predictive ability of the model was satisfactory and it can be used for designing similar groups of anti- bacterial compounds.