Prediction of enhancement effect of nitroimidazoles on irradiation by gene expression programming

Abstract

A novel machine learning method, gene expression programming(GEP), was employed to build quatitative structure-activity relationship(QSAR) models for predicting the enhancement effect of nitroimidazole compounds on irradiation. The models were based on descriptors which were calculated from the molecular structures. Four descriptors were selected from the pool of descriptors by best multiple linear regression(BMLR) method. After that, three regression methods, multiple linear regression(MLR), support vector machine(SVM) and GEP, were used to build QSAR models. Compared to MLR and SVM, GEP produced a better model with the square of correlation coefficient(R ²), 0.9203 and 0.9014, and the root mean square error(RMSE), 0.6187 and 0.6875, for training set and test set, respectively. The results show that the GEP model has better predictive ability and more reliable than the MLR and SVM models. This indicates that GEP is a promising method on relevant researches in radiation area.

Keywords

Nitroimidazole / Irradiation enhancement effect / Gene expression programming

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