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

• Published in: Chemical product and process modeling Vol. 19; no. 2; pp. 251 - 262
• Main Authors: Bouarra, Nabil, Kherouf, Soumaya, Nadji, Nawel, Nouri, Loubna, Boudjemaa, Amel, Djerad, Souad, Bachari, Khaldoun
• Format: Journal Article
• Language: English
• Published: De Gruyter 16.05.2024
• Subjects: ANN modeling; anticancer agents; aziridinylquinones; half wave potential; QSER
• ISSN: 1934-2659; 1934-2659
• DOI: 10.1515/cppm-2023-0024

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 ( ) and Root Mean Squared Error of prediction (RMSE) were mentioned to demonstrate the QSER model’s prediction abilities. Calculated and RMSE values for the MLR model were 0.858 and 0.054, respectively. The and RMSE 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.