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 (Q² _CV = 0.8676, RSR_CV = 0.3518). Internal validation showed strong predictive stability, while external validation demonstrated the model’s generalizability with a Q² _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.

Acorn flours from the six Mediterranean Quercus species (Quercus cerris, Quercus petraea, Quercus robur, Quercus ilex, Quercus pubescens, and Quercus rotundifolia) were systematically fractionated for polyphenols using ultrasonic-assisted extraction with seven solvent systems varying in polarity and composition (water at 20 and 40 min; acetone, ethanol, and methanol at 20% and/or 70% v/v). The total polyphenol content (TPC), non-tannic phenolic content (NTPC), tannin content (TC), antioxidant potential (DPPH, ABTS, and FRAP), and individual phenolic profiles through high-performance liquid chromatography (HPLC) were determined. The results showed that the botanical species primarily determined the TPC and TC, while the solvent composition significantly influenced the NTPC yield. Q. cerris yielded the highest average TPC (105.1 ± 3.8 mg GAE/g) and TC, supported by a gallotannin-dominated profile. Conversely, Q. rotundifolia exhibited the lowest values but the highest NTPC/TPC ratio (32.0%). Q. ilex featured species-exclusive ellagitannins, while Q. pubescens showed the highest specific antioxidant activity. For the targeted recovery, 20% acetone is recommended for tannins and 70% ethanol for the non-tannic fractions. These findings establish a species-resolved framework for valorizing acorn flours as functional ingredients, identifying high-tannin species requiring detannification and “sweet” varieties suitable for direct food application.