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Predicting organic chemical biodegradability according to logistic regression method

A technology of organic chemicals and biodegradability, applied in the field of ecological risk assessment and testing strategies, can solve the problems of inability to extract prediction rules, unfavorable model application and mechanism interpretation, and poor comprehension, so as to facilitate analysis and understanding and practical application, Good fitting effect, effect of transparent prediction rules

Active Publication Date: 2013-10-09
DALIAN UNIV OF TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Therefore, both models have a "black box" nature, cannot extract prediction rules, and have poor comprehensibility, which is not conducive to model application and mechanism interpretation

Method used

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  • Predicting organic chemical biodegradability according to logistic regression method
  • Predicting organic chemical biodegradability according to logistic regression method
  • Predicting organic chemical biodegradability according to logistic regression method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0028] Given the compound 4-aminopyridine (SMILES:Nc1ccncc1), predict its biodegradability. First, according to the molecular structure of 4-aminopyridine, use Draogon software (Version6.0) to calculate 14 kinds of descriptors nN, nHM, O%, MATS1e, GATS1p, GATS7p, GGI1, GGI2, nCq, nCrt, C-040, H The values ​​of -048, H-051 and O-059 are 2, 0, 0.246, 0.914, 0, 1, 0.444, 0, 0, 0, 0, 0 and 0, respectively. According to the formula (2), the Euclidean distance of the eigenvector is 0.399 (<1.628). Within the scope of the model application domain, the model can be used to predict the biodegradability of 4-aminopyridine. Substituting the descriptor values ​​into the built model has:

[0029] z=1.9025+1.0457×2+0.6662×0-0.1078×0+2.8362×(-0.246)-2.0019×0.914-0.7015×0+0.1131×1+0.7023×0.444+2.7793×0+1.035×0-0.777×0 -0.7091×0-0.1553×0+0.955×0=-2.961

[0030] but The biodegradability of 4-aminopyridine was predicted to be difficult to degrade, which was consistent with the experimental ...

Embodiment 2

[0032] Given the compound 4-methoxyphenol (SMILES: O(c(ccc(O)c1)c1)C), the values ​​of 14 descriptors calculated by Draogon software are 0, 0, 11.8, -0.11, 1.114 , 0.528, 2, 0.889, 0, 0, 0, 0, 0, and 0. The Euclidean distance of the eigenvector calculated according to the molecular structure descriptor value is 0.219 (<1.628). Within the scope of the model application domain, the model can be used to predict the biodegradability of 4-methoxyphenol. The obtained descriptor values ​​were substituted into the model to obtain f(z)=0.193<0.500, and the biodegradability of 4-methoxyphenol was predicted to be easy to degrade, which was consistent with the experimental results.

Embodiment 3

[0034] Given the compound bromopentane (SMILES:CCCCCBr), predict its biodegradability. The 14 descriptor values ​​were calculated using Draogon software as 0, 1, 0, -0.015, 0.921, 0, 0.5, 0.222, 0, 0, 0, 0, 0 and 0. The Euclidean distance of the bromopentane eigenvector calculated according to the molecular structure descriptor value is 0.351 (0.500, and the biodegradability of bromopentane was predicted to be difficult to degrade, which was consistent with the experimental results.

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Abstract

The invention discloses a method for predicting organic chemical biodegradability according to a logistic regression algorithm. According to the method for predicting organic chemical biodegradability, on the basis that the molecular structure of a compound is obtained, a person just needs to calculate descriptors of representational structure characteristics and use a built quantitative structure-activity relationship (QSAR) model, and accordingly the biodegradability of the organic compound can be fast and efficiently predicted. The method for predicting organic chemical biodegradability is low in cost, and easy and convenient and fast to adopt, and saves large required labor sources, cost and time. According to the method for predicting organic chemical biodegradability, modeling completely accords with the QSAR model building and guidelines for use of the Organization for Economic Co-operation and Development (OECD), only 14 molecular structure descriptors are adopted, the logistic regression method which is clear and transparent in algorithm is applied, and therefore the method for predicting organic chemical biodegradability is easy to understand and apply. Model application fields are explicit, and 1629 kinds of compounds are covered. The method for predicting organic chemical biodegradability according to the logistic regression method has good fitting effect, robustness and prediction ability, can effectively predict biodegradability of a plurality of organic compounds and provide important data support to organic chemical risk assessment and management, and has important significance in ecological risk assessment.

Description

technical field [0001] The invention relates to a method of predicting the biodegradability of organic chemicals by using a logistic regression algorithm, and belongs to the field of ecological risk evaluation test strategies. Background technique [0002] Microbes in the environment can destroy the molecular structure of some organic matter or mineralize it through oxidation, reduction, and hydrolysis, and remove organic matter from the environment. This process is biodegradation, which is an important way to remove pollutants from the environment and affects the environmental persistence and fate of pollutants. my country promulgated the "Environmental Management Measures for New Chemical Substances" in September 2003, and revised it in October 2010, requiring the identification of the environmental persistence and other properties of new chemicals, and then approval and necessary approval based on the results obtained. time limit. Biodegradability is an important paramet...

Claims

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Application Information

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IPC IPC(8): G06F17/50
Inventor 李雪花陈广超陈景文乔显亮
Owner DALIAN UNIV OF TECH
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