Inter‐Hammett: Enhancing Interpretability in Hammett‘s Constant Prediction via Extracting Rules

Abstract The Hammett constants () describe the electron‐withdrawing and electron‐donating effects of substituents in aromatic compounds and are widely used in structure–activity relationship studies. However, their experimental determination is resource‐intensive and time‐consuming. Although graph neural networks (GNNs), such as GCN and Weave, have been proposed for predicting Hammett constants using graph‐based features, they suffer from poor interpretability. To address limited interpretability, we introduce Inter‐Hammett , a framework designed to enhance interpretability while maintaining high predictive performance. Inter‐Hammett leverages cheminformatics‐derived descriptors from RDKit, Mordred, PyBioMed, and CDK, followed by rigorous AutoGluon‐based feature selection to mitigate the curse of dimensionality. The model core is trained using RuleFit on 85% of the dataset, ensuring a balance between accuracy and interpretability. On unseen data, Inter‐Hammett achieved an R 2 of 0.880 and an RMSE of 0.128 , outperforming eleven models, including four recently published state‐of‐the‐art deep learning approaches. Additionally, a comprehensive interpretability analysis using seven different methods further enhances transparency, making Inter‐Hammett a robust alternative for Hammett's constant prediction.