Method for adopting quantitative structure-activity relationship model to predicting soil or sediment adsorption coefficients of organic compound

Abstract

The invention discloses a method for adopting a quantitative structure-activity relationship model to predicting soil or sediment adsorption coefficients of an organic compound. On the basis that a molecular structure of the organic compound is known, the soil or sediment adsorption coefficients of the organic compound can be quickly and efficiently predicted only by calculating a molecular descriptor with the molecular structure and applying a built QSAR (quantitative structure-activity relationship) model. The method is simple, quick, low in cost and capable of saving manpower, material resources and financial resources needed for experiment testing. Modeling is performed according to guidelines on building and using the QSAR model of the Organization for Economic Cooperation and Development, and a simple and transparent multiple linear regression analysis method is applied, so that easiness in understanding and applying is realized; the method has clear application domain, good fitting capacity, robustness and predicting capability; by the method, the soil or sediment adsorption coefficients of the organic compound in the application domain can be effectively predicted, and necessary basic data are provided for ecological risk evaluation and management of the compound; the method has important significance.

Description

technical field [0001] The invention belongs to the field of ecological risk assessment testing strategies, and relates to a method for predicting the soil / sediment adsorption coefficient of organic compounds by using a quantitative structure-activity relationship model. Background technique [0002] The soil / sediment adsorption coefficient is an important parameter to characterize the adsorption, distribution behavior and environmental fate of organic compounds between soil / sediment and water phase, and the distribution coefficient K p said, K p =C S / C W , where C S and C W Represent the concentration of organic compounds in soil / sediment and water when they reach partition equilibrium, respectively. Due to the various types and complex compositions of soil / sediment, in order to characterize the distribution behavior more accurately, a standardized partition coefficient K was introduced oc , K oc =K p / X oc , X oc Indicates the content of organic carbon in soil / s...

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Problems solved by technology

For example, in the literature "J.Environ.Sci.Health., Part A, 2012,47:441–449." Aiming at 209 kinds of polychlorinated trans-azobenzene compounds, a QSPR model was established by using genetic algorithm-artificial neural network. logK of the compound-like oc The model has good fitting ability and predictive ability, but the algorithm is opaque, which is not conducive to mechanism explanation

The document "Environ.Sci.Technol.,2006,40(22):7005–7011." collected the logK of 571 organic compounds oc Value, using multiple linear regression (MLR) analysis to establish a model, although the algorithm is transparent, it uses 29 parameters, which is not easy to use

The document "QSAR Comb Sci,2009,28(5):561–567." collected the logK of the same 571 organic compounds oc Value, using the method of least squares support vector machine to establish the model, the fitting ability and predictive ability of the model are greatly improved compared with the model established by the MLR method, but the algorithm is not transparent enough and not easy to apply

The literature "Chemosphere, 2012, 86:634–640." uses only the logP value calculated from the molecular structure as a variable, and builds a linear model to predict logK for compounds with logP in the range of 0.5–7.5 oc Value, for compounds with logP7.5, a nonlinear model is established for prediction, which greatly reduces the number of descriptors and is convenient to use, but the fitting ability of the model is not as good as the previous model

In summary, the current existing models have the shortcomings of opaque algorithms or many model parameters, and cannot analyze the logK values ​​of brominated flame retardants, mycotoxins, and phytoestrogens. oc value for accurate prediction

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