Prediction of Acute Aquatic Toxicity toward Daphnia Magna by using the GA- k NN Method
Matteo Cassotti, Davide Ballabio, Viviana Consonni, Andrea Mauri, Igor V. Tetko, Roberto Todeschini · Alternatives to Laboratory Animals · 2014
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 (R(2) and Q(2) cv equal to 0.78, Q(2) ext equal to 0.72). It comprised eight molecular descriptors that encoded information about lipophilicity, the formation of H-bonds, polar surface area, polarisability, nucleophilicity and electrophilicity.