QSER modeling of half-wave oxidation potential of indolizines by theoretical descriptors

Year 2022, Volume: 9 Issue: 3, 709 - 720, 31.08.2022

Nabil Bouarra , Nawel Nadji Soumaya Kherouf Loubna Nouri Amel Boudjemaa Khaldoun Bachari Djelloul Messadi

https://doi.org/10.18596/jotcsa.1065043

Cited By: 3

Abstract

Indolizine derivatives hold essential biological functions and have been researched for hypoglycemic, antibacterial, anti-inflammatory, analgesic, and anti-tumor actions. Indolizine scaffold has intrigued conjecture and continuous attention and has become an effective parent system for generating powerful novel medication candidates. This research focused on applying the quantitative structure-electrochemistry relationship (QSER) approach to the half-wave potential (E1/2) for Indolizine derivatives using theoretical molecular descriptors. After calculating the descriptors and splitting the data into both sets, training and prediction. The QSER model was constructed using the Genetic Algorithm/Multiple Linear Regression (GA/MLR) technique, which was used to choose the optimal descriptors for the model. A four-parameter model has been established. Many assessment procedures, including cross-validation, external validation, and Y-scrambling testing, were used to assess the model's performance. Furthermore, the applicability domain (AD) was investigated using the Williams and Insubria graphs to assess the correctness of the established model's predictions. The constructed model exhibits great goodness-of-fit to experimental data, as well as high stability (R²=0.893, Q²LOO= 0.851, Q²LMO=0.843 RMSEtr= 0.052, s= 0.056). Prediction results show a good agreement with the experimental data of E1/2 (R²ext= 0.912, Q²F1= 0.883, Q²F2= 0.883, Q²F3= 0.919, CCCext= 0.942, RMSEext=0.045).

Keywords

QSER, cyclic voltammetry, indolizines, molecular descriptors, MLR

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