Prediction of Acute Aquatic Toxicity toward Daphnia Magna by using the GA- k NN Method

Abstract

In this study, a QSAR model was developed from a data set consisting of 546 organic molecules, to predict acute aquatic toxicity toward Daphnia magna. A modified k-Nearest Neighbour (kNN) strategy was used as the regression method, which provided prediction only for those molecules with an average distance from the k nearest neighbours lower than a selected threshold. The final model showed good performance (R² and Q²_cv equal to 0.78, Q²_ext equal to 0.72). It comprised eight molecular descriptors that encoded information about lipophilicity, the formation of H-bonds, polar surface area, polaris-ability, nucleophilicity and electrophilicity.