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.
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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