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

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