Monte Carlo technique to study the adsorption affinity of azo dyes by applying new statistical criteria of the predictive potential

ABSTRACT

Azo dyes are broadly used in different industries through their chemical stability and ease of synthesis. However, these dyes are usually identified as critical environmental pollutants. Hence, a mathematical model for the adsorption affinity of azo dyes can be applied for solving tasks of medicine and ecology. Quantitative structure-property relationships for the adsorption affinity of azo dyes to a substrate (DAF, kJ/mol) were established using the Monte Carlo method by generating optimal SMILES-based descriptors. The index of ideality of correlation (IIC) and the correlation intensity index (CII) improved the model’s predictive potential, especially when they were used simultaneously. The statistical quality of the best model on the validation set was characterized by n = 18, r² = 0.9468, and RMSE = 1.26 kJ/mol.

KEYWORDS:

• Dye affinity
• azo dyes
• QSPR
• computational chemistry
• CORAL software
• correlation intensity index

Acknowledgements

The authors are grateful for the contribution of the CONCERT REACH (LIFE17 GIE/IT/000461) for support.

Disclosure statement

The authors declare that they have no known competing financial interests or personal relationships that could have influenced the work reported in this paper.

Data availability statement

The data used in this work and the models developed are freely available in the Supplementary materials section and at: http://www.insilico.eu/coral.

Supplementary material

Supplemental data for this article can be accessed at: https://doi.org/10.1080/1062936X.2022.2104369.

Additional information

Funding

This work was supported by the LIFE-CONCERT [LIFE17 GIE/IT/000461].