Prediction of Acute Aquatic Toxicity Toward Daphnia magna by using the GA-kNN Method

///Prediction of Acute Aquatic Toxicity Toward Daphnia magna by using the GA-kNN Method

Prediction of Acute Aquatic Toxicity Toward Daphnia magna by using the GA-kNN Method

Matteo Cassotti, Davide Ballabio, Viviana Consonni, Andrea Mauri, Igor V. Tetko, and Roberto Todeschini

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, Q2ext 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.

See also correction  at  http://www.atla.org.uk/corrigendum-atla-42-31-51-2014/

You need to register (for free) to download this article. Please log in/register here.