title: QSAR modeling with the electrotopological state indices: predicting the toxicity of organic chemicals.
abstract: A quantitative structure-activity relationship model, based on the atom-type electrotopological state (E-state) indices, for the prediction of toxicity to fathead minnow for a diverse set of 140 organic chemicals is presented. Multiple linear regression and artificial neural network techniques were employed in the modeling of experimental toxicity (-logLC(50)) values ranging from 0.85 to 6.09. For the training set of 130 organic compounds a linear regression model with r(2)=0.84 and s=0.36 was obtained with 14 atom-type E-state indices. For the test set of 10 compounds, the corresponding statistics were r(2)=0.83 and s=0.47, respectively. Neural networks gave a significant improvement using the same set of parameters, and the standard deviations were s=0.31 for the training set and s=0.30 for the test set when an artificial neural network with five neurons in the hidden layer was used. The results clearly show that accurate models can be rapidly calculated for the prediction of toxicity for a diverse set of organic chemicals using easily calculated parameters.
authors.affiliation: Pharmaceutical Chemistry Division, Department of Pharmacy, University of Helsinki, P.O. Box 56, FIN-00014, Finland. jarmo.huuskonen@uusiapteekki.inet.fi
authors.forename: Jarmo
authors.initials: J
authors.lastname: Huuskonen
mesh_terms: D000818:Animals; D003530:Cyprinidae; D004583:Electrons; D005544:Forecasting; D016571:Neural Networks, Computer; D009930:Organic Chemicals / Q000633:toxicity; D013329:Structure-Activity Relationship; D018675:Toxicity Tests; D015262:Xenobiotics / Q000633:toxicity*
chemical_list: D009930:Organic Chemicals; D015262:Xenobiotics