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 Q2cv 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.
Cassotti, M., Ballabio, D., Consonni, V., Mauri, A., Tetko, I., Todeschini, R. (2014). Prediction of acute aquatic toxicity toward Daphnia magna by using the GA-kNN method. ATLA. ALTERNATIVES TO LABORATORY ANIMALS, 42(1), 31-41 [10.1177/026119291404200106].
Prediction of acute aquatic toxicity toward Daphnia magna by using the GA-kNN method
CASSOTTI, MATTEO;BALLABIO, DAVIDE;CONSONNI, VIVIANA;MAURI, ANDREA;TODESCHINI, ROBERTO
2014
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 Q2cv 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.
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