Prediction of acute aquatic toxicity toward Daphnia magna by using the GA-kNN method.
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.
University of Milano-Bicocca, Department of Earth and Environmental Sciences, Milano, Italy.
Matteo
M
Cassotti
Davide
D
Ballabio
Viviana
V
Consonni
Andrea
A
Mauri
Igor V
IV
Tetko
Roberto
R
Todeschini
D000818:Animals; D003621:Daphnia / Q000187:drug effects*; D009930:Organic Chemicals / Q000633:toxicity*; D021281:Quantitative Structure-Activity Relationship; D012044:Regression Analysis; D023382:Toxicity Tests, Acute / Q000379:methods*
D009930:Organic Chemicals