High-accuracy QSAR models of narcosis toxicities of phenols based on various data partition, descriptor selection and modelling methods

Zhou, Wei; Fan, Yanjun; Cai, Xunhui; Xiang, Yan; Jiang, Peng; Dai, Zhijun; Cao, Yuan; Tan, Siqiao; Yuan, Zheming

doi:10.1039/c6ra21076g

Cited by 4 publications

(1 citation statement)

References 35 publications

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“…Quantitative structure-activity relationship (QSAR) models have been applied widely in chemical sciences such as biochemistry, environmental chemistry, food chemistry, and pharmacology. 1 Water pollution is a global concern, and developing efficient procedures for assessing the toxicity of organic pollutants to aquatic organisms has become a research priority. 2,3 QSAR can model referential activity and toxicity for an unknown compound by computing statistical relationships between biological activities and molecular descriptors (features) for a set of chemical compounds.…”

Section: Introductionmentioning

confidence: 99%

Chi-MIC-share: a new feature selection algorithm for quantitative structure–activity relationship models

Dai

Cao

et al. 2020

RSC Adv.

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Section: Introductionmentioning

confidence: 99%

Chi-MIC-share: a new feature selection algorithm for quantitative structure–activity relationship models

Dai

Cao

et al. 2020

RSC Adv.

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A novel hybrid machine learning model for predicting rate constants of the reactions between alkane and CH3 radical

Shan

Song

et al. 2022

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Prediction of pKa Values for Neutral and Basic Drugs based on Hybrid Artificial Intelligence Methods

Zhang

Chen

et al. 2018

Sci Rep

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The pKa value of drugs is an important parameter in drug design and pharmacology. In this paper, an improved particle swarm optimization (PSO) algorithm was proposed based on the population entropy diversity. In the improved algorithm, when the population entropy was higher than the set maximum threshold, the convergence strategy was adopted; when the population entropy was lower than the set minimum threshold the divergence strategy was adopted; when the population entropy was between the maximum and minimum threshold, the self-adaptive adjustment strategy was maintained. The improved PSO algorithm was applied in the training of radial basis function artificial neural network (RBF ANN) model and the selection of molecular descriptors. A quantitative structure-activity relationship model based on RBF ANN trained by the improved PSO algorithm was proposed to predict the pKa values of 74 kinds of neutral and basic drugs and then validated by another database containing 20 molecules. The validation results showed that the model had a good prediction performance. The absolute average relative error, root mean square error, and squared correlation coefficient were 0.3105, 0.0411, and 0.9685, respectively. The model can be used as a reference for exploring other quantitative structure-activity relationships.

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High-accuracy QSAR models of narcosis toxicities of phenols based on various data partition, descriptor selection and modelling methods

Abstract: The environmental protection agency thinks that quantitative structure–activity relationship (QSAR) analysis can better replace toxicity tests.

Cited by 4 publications

References 35 publications

Chi-MIC-share: a new feature selection algorithm for quantitative structure–activity relationship models

Chi-MIC-share: a new feature selection algorithm for quantitative structure–activity relationship models

A novel hybrid machine learning model for predicting rate constants of the reactions between alkane and CH3 radical

Prediction of pKa Values for Neutral and Basic Drugs based on Hybrid Artificial Intelligence Methods

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