Python-based QSAR modeling protocol for antioxidant activity: a case-study using a library of di(hetero)cyclic amines or amides

Abstract Constructing a QSAR model involves several critical steps, including chemical structure preparation, molecular descriptor calculation and selection, and model development and validation. This study presents a comprehensive methodology for preparing QSAR models using freely open-source software tools. A detailed, step-by-step protocol outlines the entire process, from compound library preparation to statistical validation. As a case study, we developed a QSAR model to predict the antioxidant activity, specifically radical scavenging activity, of 70 di(hetero)aryl amine and amide compounds. Molecular descriptors (12,072 total) were calculated using the OCHEM platform, and PyQSAR built-in tools were used for descriptor selection and model construction. Four key descriptors (B06[C-O], Eig04_AEA(dm), JGI2, and J_Dz(p)) were selected to develop a MLR model with strong statistical performance (Q 2 CV = 0.8676, RSR CV = 0.3518). Internal validation showed strong predictive stability, while external validation demonstrated the model’s generalizability with a Q 2 EXT > 0.5. This study not only demonstrates the application of a freely open-source QSAR approach but also contributes to ongoing efforts in identifying and designing potent antioxidant agents with potential therapeutic applications. All relevant files and the detailed protocol are provided, allowing other researchers to replicate the antioxidant QSAR model and apply the methodology to develop QSAR models for other compound libraries and biological activities.