Use of the index of ideality of correlation to improve models of eco-toxicity

Persistent organic pollutants are compounds used for various everyday purposes, such as personal care products, food, pesticides, and pharmaceuticals. Decomposition of considerable part of the above pollutants is a long-time process. Under such circumstances, estimation of toxicity for large arrays of organic substances corresponding to the above category of pollutants is a necessary component of theoretical chemistry. The CORAL software is a tool to establish quantitative structure—activity relationships (QSARs). The index of ideality of correlation (IIC) was suggested as a criterion of predictive potential of QSAR. The statistical quality of models for eco-toxicity of organic pollutants, which are built up, with use of the IIC is better than statistical quality of models, which are built up without use of data on the IIC.

[1]  K. Roy,et al.  On Two Novel Parameters for Validation of Predictive QSAR Models , 2009, Molecules.

[2]  K. Roy,et al.  Further exploring rm2 metrics for validation of QSPR models , 2011 .

[3]  B. Nowack,et al.  Procedures for the production and use of synthetically aged and product released nanomaterials for further environmental and ecotoxicity testing , 2018 .

[4]  F. Sánchez‐Bayo.  Comparative acute toxicity of organic pollutants and reference values for crustaceans. I. Branchiopoda, Copepoda and Ostracoda. , 2006, Environmental pollution.

[5]  Andrey A Toropov,et al.  The index of ideality of correlation: A criterion of predictability of QSAR models for skin permeability? , 2017, The Science of the total environment.

[6]  J. Sierra,et al.  Quantitative structure-activity relationship (QSAR) prediction of (eco)toxicity of short aliphatic protic ionic liquids. , 2015, Ecotoxicology and environmental safety.

[7]  O. Raevsky,et al.  QSAR models of the inhalation toxicity of organic compounds , 2011, Pharmaceutical Chemistry Journal.

[8]  Francisco Torrens,et al.  A novel approach to predict aquatic toxicity from molecular structure. , 2008, Chemosphere.

[9]  Feng Luan,et al.  Computational ecotoxicology: simultaneous prediction of ecotoxic effects of nanoparticles under different experimental conditions. , 2014, Environment international.

[10]  Andrey A. Toropov,et al.  Index of Ideality of Correlation: new possibilities to validate QSAR: a case study , 2018, Structural Chemistry.

[11]  M. Natália D. S. Cordeiro,et al.  Probing the toxicity of nanoparticles: a unified in silico machine learning model based on perturbation theory , 2017, Nanotoxicology.

[12]  Andrey A Toropov,et al.  The index of ideality of correlation: A criterion of predictive potential of QSPR/QSAR models? , 2017, Mutation research.

[13]  V. V. Kleandrova,et al.  Computational tool for risk assessment of nanomaterials: novel QSTR-perturbation model for simultaneous prediction of ecotoxicity and cytotoxicity of uncoated and coated nanoparticles under multiple experimental conditions. , 2014, Environmental science & technology.

[14]  C. Venkataraman,et al.  Toxicity assessment of organic pollutants: reliability of bioluminescence inhibition assay and univariate QSAR models using freshly prepared Vibrio fischeri. , 2008, Toxicology in vitro : an international journal published in association with BIBRA.

[15]  L. Lomba,et al.  Ecotoxicity and QSAR studies of glycerol ethers in Daphnia magna. , 2017, Chemosphere.

[16]  Andrey A. Toropov,et al.  Application of the Monte Carlo method for building up models for octanol-water partition coefficient of platinum complexes , 2018, Chemical Physics Letters.

[17]  Andrey A Toropov,et al.  CORAL software: prediction of carcinogenicity of drugs by means of the Monte Carlo method. , 2014, European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences.

[18]  P. Gramatica,et al.  Ranking of aquatic toxicity of esters modelled by QSAR. , 2005, Chemosphere.

[19]  L. Lin,et al.  A concordance correlation coefficient to evaluate reproducibility. , 1989, Biometrics.

[20]  Francisco Torrens,et al.  Prediction of Aquatic Toxicity of Benzene Derivatives to Tetrahymena pyriformis According to OECD Principles. , 2016, Current pharmaceutical design.

[21]  M. Scotti,et al.  Predictive ecotoxicity of MoA 1 of organic chemicals using in silico approaches. , 2018, Ecotoxicology and environmental safety.

[22]  T. H. Christensen,et al.  Toxicity of organic chemical pollution in groundwater downgradient of a landfill (Grindsted, Denmark). , 2000 .

[23]  H. Zhai,et al.  Predicting the ecotoxicity of ionic liquids towards Vibrio fischeri using genetic function approximation and least squares support vector machine. , 2015, Journal of hazardous materials.