Modeling of acute aqueous toxicity of organic compounds for Daphnia magna - Vazhev, V.V.; Munarbaeva, B.G.; Yergaliyeva, E.M.; Vazheva, N.V.; Gubenko, M.A. (Ye.A.Buketov Karaganda State University Publishing house, 2018)
Methods for the assessment of the environmental hazard of chemicals include the detection of acute aquatic toxicity of compounds for various small organisms, these are test objects. Experimental determination of toxicity presents significant difficulties; therefore, computational methods are developed. The article is devoted to predicting the toxicity parameter of organic substances of different classes in relation to the test organism from the class of crustaceans Daphnia magna. The possibility of using QSAR / QSTR (Quantitative Structure Activity Relationships / Quantitative Structure Toxicity Relationships) method for estimating the toxicity index of pLC50 using a large set of descriptors was investigated. To describe the structure of molecules, a set of 2644 descriptors generated by the Dragon 7 program was used. The calculations were carried out using the computer program Program Robustness Calculation (PROGROC). This program allows the number of descriptors to be used without preliminary selection exceeding the number of substances in the set. 546 Substances were used to construct the models, 170 compounds of which made up a control set, and 376 entered the training set. The obtained models are characterized by a high correlation between calculation and experiment. For a control sample of 170 compounds, the correlation coefficient is R = 0.952 and the standard deviation is s = 0.45, for a training sample of 376 compounds, R = 0.971 and s = 0.41. The model for all samples is characterized by the following statistical parameters: correlation coefficient is R = 0.966 and standard deviation is s = 0.42.
Daphnia magna үшін органикалық қосылыстардың өткір сулы улылығын модельдеу
Моделирование острой водной токсичности органических соединений для Daphnia magna
Keywords: organic compounds; toxicity; modeling; QSAR/ QSTR; predicting; pLC50; Daphnia magna; PROGROC