Startseite Naturwissenschaften Quantitative structure-electrochemistry relationship modeling of a series of anticancer agents using MLR and ANN approaches
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Quantitative structure-electrochemistry relationship modeling of a series of anticancer agents using MLR and ANN approaches

  • Nabil Bouarra ORCID logo EMAIL logo , Soumaya Kherouf ORCID logo , Nawel Nadji , Loubna Nouri , Amel Boudjemaa , Souad Djerad und Khaldoun Bachari ORCID logo EMAIL logo
Veröffentlicht/Copyright: 27. Februar 2024
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Chemical Product and Process Modeling
Aus der Zeitschrift Chemical Product and Process Modeling Band 19 Heft 2

Abstract

QSPR is a powerful tool for elucidating the correlation between chemical structure and property for both natural and synthesized compounds. In the present work, the half-wave reduction potential for a set of aziridinylquinones (Anticancer Agents [AA]) is modelled using a quantitative structure-electrochemistry relationship (QSER) based on multilinear regression (MLR) and artificial neural network (ANN). Molecular descriptors introduced in this work were computed using the Dragon software (V5). Before the model’s generation, using the Kennard and Stone algorithm, the data set of 84 aziridinylquinones was divided into training and prediction sets consisting of 70 % and 30 % of data points. Quantitative Structure Electrochemistry Relationship (QSER) models were developed using the Genetic Algorithm Multiple Linear Regressions (GA-MLR) and an Artificial Neural Network (ANN). The coefficient of determination (R 2) and Root Mean Squared Error of prediction (RMSE) were mentioned to demonstrate the QSER model’s prediction abilities. Calculated R 2 and RMSEval values for the MLR model were 0.858 and 0.054, respectively. The R 2 and RMSEval values for the ANN training set were calculated to be 0.914 and 0.050, respectively. Findings show that GA is a powerful tool for selecting variables in QSER analysis. Comparing the two employed regression methods showed that ANN is superior to MLR in predictive ability.

Keywords: QSER; anticancer agents; half wave potential; aziridinylquinones; ANN modeling
Corresponding authors: Nabil Bouarra, Centre de Recherche Scientifque et Technique en Analyse Physico-Chimique (CRAPC), BP 384 Tipasa, RP 42004, Bou – Ismail, Algiers, Algeria; and Department of Process Engineering, Laboratory of Environmental Engineering, Faculty of Engineering Sciences, Badji Mokhtar University, PB 12, Annaba 23000, Algeria, E-mail: ; and Khaldoun Bachari, Centre de Recherche Scientifque et Technique en Analyse Physico-Chimique (CRAPC), BP 384 Tipasa, RP 42004, Bou – Ismail, Algiers, Algeria, E-mail:

Acknowledgments

We thank Prof. Paola Gramatica for the free license of QSARINS.

  1. Research ethics: Not applicable.

  2. Author contributions: The authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  3. Competing interests: The authors state no conflict of interest.

  4. Research funding: This work is supported by the Directorate General for Scientific Research and Technological Development DGRSDT (Algeria).

  5. Data availability: Not applicable.

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Supplementary Material

This article contains supplementary material (https://doi.org/10.1515/cppm-2023-0024).

Received: 2023-03-16
Accepted: 2024-02-06
Published Online: 2024-02-27

© 2024 Walter de Gruyter GmbH, Berlin/Boston

Artikel in diesem Heft

  1. Frontmatter
  2. Editorial
  3. CPPM special issue in honor of Professor Faïçal Larachi
  4. Research Articles
  5. Predicting buoyant jet characteristics: a machine learning approach
  6. Insights into the bubble formation dynamics in converging shape microchannels using CLSVOF method
  7. Comparison of different CFD approaches for the simulation of developing free surface two-phase flow in straight and bent pipes
  8. A new approach to model the fluid dynamics in sandwich packings
  9. Effect of novel mixed impeller on local bubble size and flow regime transition in pilot scale gas-liquid stirred tank reactor
  10. Quantitative structure-electrochemistry relationship modeling of a series of anticancer agents using MLR and ANN approaches
  11. Extractive desulfurization of crude petroleum oil and liquid fuels using trihexyl tetradecyl phosphonium bis(2-ethylhexyl) phosphate ionic liquid
  12. CFD-aided contraction-expansion static mixer design for oil-in-water emulsification
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https://doi.org/10.1515/cppm-2023-0024
Schlagwörter für diesen Artikel
QSER; anticancer agents; half wave potential; aziridinylquinones; ANN modeling

Artikel in diesem Heft

  1. Frontmatter
  2. Editorial
  3. CPPM special issue in honor of Professor Faïçal Larachi
  4. Research Articles
  5. Predicting buoyant jet characteristics: a machine learning approach
  6. Insights into the bubble formation dynamics in converging shape microchannels using CLSVOF method
  7. Comparison of different CFD approaches for the simulation of developing free surface two-phase flow in straight and bent pipes
  8. A new approach to model the fluid dynamics in sandwich packings
  9. Effect of novel mixed impeller on local bubble size and flow regime transition in pilot scale gas-liquid stirred tank reactor
  10. Quantitative structure-electrochemistry relationship modeling of a series of anticancer agents using MLR and ANN approaches
  11. Extractive desulfurization of crude petroleum oil and liquid fuels using trihexyl tetradecyl phosphonium bis(2-ethylhexyl) phosphate ionic liquid
  12. CFD-aided contraction-expansion static mixer design for oil-in-water emulsification
  13. Liquid-liquid flow pattern and mass transfer in a rotating millimeter channel reactor
  14. By-product Eucalyptus leaves valorization in the basic dye adsorption: kinetic equilibrium and thermodynamic study
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