Prediction of fathead minnow acute toxicity of organic compounds from molecular structure.
Interest in the prediction of toxicity without the use of experimental data is growing, and quantitative structure-activity relationship (QSAR) methods are valuable for such predictions. A QSAR study of acute aqueous toxicity of 375 diverse organic compounds has been developed using only calculated structural features as independent variables. Toxicity is expressed as -log(LD(50)) with the units -log(millimoles per liter) and ranges from -3 to 6. Multiple linear regression and computational neural networks (CNNs) are utilized for model building. The best model is a nonlinear CNN model based on eight calculated molecular structure descriptors. The root-mean-square log(LD(50)) errors for the training, cross-validation, and prediction sets of this CNN model are 0.71, 0.77, and 0.74 -log(mmol/L), respectively. These results are compared to a previous study with the same data set which included many more descriptors and used experimental data in the descriptor pool.
Department of Chemistry, 152 Davey Laboratory, The Pennsylvania State University, University Park, Pennsylvania 16802, USA.
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D000818:Animals; D003530:Cyprinidae; D007928:Lethal Dose 50; D008954:Models, Biological; D009010:Monte Carlo Method; D016571:Neural Networks, Computer; D009930:Organic Chemicals / Q000633:toxicity*; D012044:Regression Analysis; D013329:Structure-Activity Relationship; D018675:Toxicity Tests
D009930:Organic Chemicals