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Research Article

In silico enhancement of azo dye adsorption affinity for cellulose fibre through mechanistic interpretation under guidance of QSPR models using Monte Carlo method with index of ideality correlation

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Pages 697-715 | Received 30 Jun 2020, Accepted 03 Aug 2020, Published online: 03 Sep 2020
 

ABSTRACT

Azo dyes are a group of chemical moieties joined by azo (-N=N-) group with potential usefulness in different industrial applications. But these dyes are not devoid of hazardous consequence because of poor affinity for the fibre and discharge into the water stream. The chemical aspects of 72 azo dyes towards cellulose fibre in terms of their affinity by QSPR have been explored in the present work. We have employed two approaches, namely balance of correlation without IIC (TF1) and balance of correlation with IIC (TF2), to generate 16 QSAR models from 8 splits. The determination coefficient of calibration and validation set was found higher when the QSPR models were developed using the index of ideality correlation (IIC) parameter (TF2). The model developed with TF2 for split 3 was considered as a prominent model because the determination coefficient of the validation set was maximum (r2 = 0.9468). The applicability domain (AD) was also analysed based on ‘statistical defect’, d(A) for a SMILES attribute. The mechanistic interpretation was done by identifying the SMILES attributes responsible for the promoter of endpoint increase and promoter of endpoint decrease. These SMILES attributes were applied to design 15 new dyes with higher affinity for cellulose fibre.

Acknowledgements

The authors thank Dr Andrey A. Toropov and Dr Alla P. Toropova for providing CORAL software. The authors are also thankful to their respective universities for providing the infrastructure.

Author contributions

Authors have done equivalent contributions to this work.

Disclosure statement

No potential conflict of interest was reported by the authors.

Data availability

The processed data required to reproduce these findings are available to download from supporting information.

Supplementary material

Supplemental data for this article can be accessed at: https://doi.org/10.1080/1062936X.2020.1806105.

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