<jats:title>Abstract</jats:title><jats:p>This article presents a quantitative structure–activity relationship (QSAR) approach for predicting the acid dissociation constant (pK<jats:inline-formula><jats:alternatives><jats:tex-math>$$_a$$</jats:tex-math><mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML">
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</mml:math></jats:alternatives></jats:inline-formula>) of nitrogenous compounds, including those within supramolecular complexes based on cucurbiturils. The model combines low-cost quantum mechanical calculations with QSAR methodology and linear regressions to achieve accurate predictions for a broad range of nitrogen-containing compounds. The model was developed using a diverse dataset of 130 nitrogenous compounds and exhibits excellent predictive performance, with a high coefficient of determination (R<jats:inline-formula><jats:alternatives><jats:tex-math>$$^2$$</jats:tex-math><mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML">
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</mml:math></jats:alternatives></jats:inline-formula>) of 0.9905, low standard error (<jats:italic>s</jats:italic>) of 0.3066, and high Fisher statistic (<jats:italic>F</jats:italic>) of 2142. The model outperforms existing methods, such as Chemaxon software and previous studies, in terms of accuracy and its ability to handle heterogeneous datasets. External validation on pharmaceutical ingredients, dyes, and supramolecular complexes based on cucurbiturils confirms the reliability of the model. To enhance usability, a script-like tool has been developed, providing a streamlined process for users to access the model. This study represents a significant advancement in pK<jats:inline-formula><jats:alternatives><jats:tex-math>$$_a$$</jats:tex-math><mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML">
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</mml:math></jats:alternatives></jats:inline-formula> prediction, offering valuable insights for drug design and supramolecular system optimization.</jats:p>
<jats:p><jats:bold>Graphical Abstract</jats:bold></jats:p>
