A feed-forward artificial neural network for prediction of the aquatic ecotoxicity of alcohol ethoxylate.
A feed-forward artificial neural network (ANN) has been developed for predicting the aquatic ecotoxicity of alcohol ethoxylate (AE), a non-ionic surfactant comprising a variety of homologues. Trained with previously reported ecotoxicity data, the ANN utilizes both molecular characteristics (alkyl chain length, branching extent in alkyl chain, and ethoxylate (EO) number) and exposure features (effect endpoint, test duration, test type, and species taxon) as inputs to predict the ecotoxicity. The ANN predicted an increase in ecotoxicity for homologues with a longer or less-branched alkyl chain, or those with fewer EO units. But for long alkyl chain (>14) homologues, the ecotoxicity increase was predicted by the ANN to level off, which is obscured by existing quantitative structure-activity relationships (QSARs). A "leave-one-out" cross-validation process indicated that the prediction accuracy was within a factor of 5 with 90% probability. This research demonstrated that the current ANN covers a wider application domain with respect to the homologue range and a variety of exposure features without compromising on predictive accuracy.
National Institute of Advanced Industrial Science and Technology, Research Center for Chemical Risk Management, 16-1 Onogawa, Tsukuba City 305-8569, Japan. yaobin-meng@aist.go.jp
Yaobin
Y
Meng
Bin-Le
BL
Lin
D000818:Animals; D001681:Biological Assay; D003198:Computer Simulation; D003621:Daphnia; D005019:Ethyl Ethers / Q000633:toxicity*; D007928:Lethal Dose 50; D008954:Models, Biological; D016571:Neural Networks, Computer; D013501:Surface-Active Agents / Q000633:toxicity*; D014874:Water Pollutants, Chemical / Q000633:toxicity*
D005019:Ethyl Ethers; D013501:Surface-Active Agents; D014874:Water Pollutants, Chemical