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QSRR models for predicting the retention indices of VOCs in different datasets using an efficient variable selection method coupled with artificial neural network modeling: ANN-based QSPR modeling

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Abstract

A combination of the smoothly clipped absolute deviation (SCAD) method and the artificial neural network (ANN) was utilized as a novel methodology (SCAD-ANN) in the quantitative structure-retention indices relationship (QSRR). The proposed SCAD method reduces the dimension of data before using the robust ANN modeling method. The efficiency of the SCAD-ANN methods was evaluated by the construction of a QSRR model between the most relevant molecular descriptors (MDs) and RIs for two sets of volatile organic compounds. The SCAD method was applied to training data, and effective MDs were selected in a λ with the lowest cross-validation error (λmin) and were defined as the inputs to the ANN modeling method. All ANN parameters were optimized simultaneously. Some statistical parameters were computed, and the obtained results indicate that the constructed QSRR models have acceptable values. Also, the applicability domain analysis reveals that more than 95% of the data are in the confidence range, indicating that the prediction results of the SCAD-ANN models are reliable.

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Availability of data and material

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Code availability

Code for data cleaning and analysis is provided as part of the replication package. It is available at "r-project.org" for review.

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Acknowledgements

The authors are thankful to the Shahrood University of Technology Research Council for supporting this work.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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Authors and Affiliations

  1. Department of Chemistry, Shahrood University of Technology, P.O. Box 36155-316, Shahrood, Iran

    Zeinab Mozafari, Mansour Arab Chamjangali & Nasser Goudarzi

  2. Department of Statistics, Faculty of Mathematical Sciences, Ferdowsi University of Mashhad, Mashhad, Khorasan Razavi, Iran

    Mohammad Arashi

Authors
  1. Zeinab Mozafari
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  2. Mansour Arab Chamjangali
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  3. Mohammad Arashi
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  4. Nasser Goudarzi
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Contributions

ZM: Methodology, Software, Writing—original draft, Investigation, Writing—review and editing. MAC: Supervision, Writing—review and editing, Data curation. MA: Methodology, Software, Validation, Writing—review and editing. NG: Review and editing.

Corresponding author

Correspondence to Mansour Arab Chamjangali.

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Mozafari, Z., Arab Chamjangali, M., Arashi, M. et al. QSRR models for predicting the retention indices of VOCs in different datasets using an efficient variable selection method coupled with artificial neural network modeling: ANN-based QSPR modeling. J IRAN CHEM SOC 19, 2617–2630 (2022). https://doi.org/10.1007/s13738-021-02488-2

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