Predicting toxic action mechanisms of phenols using AdaBoost Learner

Abstract

AdaBoost Learner is employed to investigate Structure–Activity Relationships of phenols based on molecular descriptors. In this paper, the performance of AdaBoost Learner is compared with support vector machine (SVM), artificial neural networks (ANNs) and K nearest neighbors (KNNs), which are the most common algorithms used for SARs analysis. AdaBoost Learner performed better than SVM, ANNs and KNNs in predicting the mechanism of toxicity of phenols based on molecular descriptors. It can be concluded that AdaBoost has a potential to improve the performance of SARs analysis. We believe that AdaBoost Learner will play an important and complementary role to the existing algorithms for the prediction of the mechanisms of toxicity based on SARs. We have developed an online web server for the prediction of ecotoxicity mechanisms of phenols, accessible at http://chemdata.shu.edu.cn/ecotoxity/.