Machine learning-based prediction of toxicity of organic compounds towards fathead minnow. Issue 59 (1st October 2020) - Chen, Xingmei; Dang, Limin; Yang, Hai; Huang, Xianwei; Yu, Xinliang
Abstract : A quantitative structure–toxicity relationship of 963 chemicals against fathead minnow was developed by using support vector machine and genetic algorithm. Abstract : Predicting the acute toxicity of a large dataset of diverse chemicals against fathead minnows ( Pimephales promelas ) is challenging. In this paper, 963 organic compounds with acute toxicity towards fathead minnows were split into a training set (482 compounds) and a test set (481 compounds) with an approximate ratio of 1 : 1. Only six molecular descriptors were used to establish the quantitative structure–activity/toxicity relationship (QSAR/QSTR) model for 96 hour p LC50 through a support vector machine (SVM) along with genetic algorithm. The optimal SVM model ( R 2 = 0.756) was verified using both internal (leave-one-out cross-validation) and external validations. The validation results ( q int 2 = 0.699 and q ext 2 = 0.744) were satisfactory in predicting acute toxicity in fathead minnows compared with other models reported in the literature, although our SVM model has only six molecular descriptors and a large data set for the test set consisting of 481 compounds.