Quantitative structure-toxicity relationships of organic chemicals against Pseudokirchneriella subcapitata

Abstract

Predicting the toxicity of organic toxicants to aquatic life through chemometric approach is challenging area. In this paper, a six-descriptor quantitative structure–activity/toxicity relationship (QSAR/QSTR) model was successfully developed for the toxicity pEC10 of organic chemicals against Pseudokirchneriella subcapitata, by applying support vector machine (SVM) together with genetic algorithm. A sufficiently large data set consisting of 334 organic chemicals was randomly divided into a training set (167 compounds) and a test set (167 compounds) with a ratio of 1:1. The optimal SVM model possesses coefficient of determination R2 of 0.76 and mean absolute error (MAE) of 0.60 for the training set and R2 of 0.75 and MAE of 0.61 for the test set. Compared with other models reported in the literature, our SVM model for the toxicity pEC10 shows significant statistical quality and satisfactory predictive ability, although it has fewer molecular descriptors and more samples in the test set. A QSTR model for pEC50 of organic chemicals against Pseudokirchneriella subcapitata was also developed with the same subsets and molecular descriptors.