A machine learning-driven prediction of Hammett constants using quantum chemical and structural descriptors
Vaneet Saini, Ranjeet Kumar · Physical Chemistry Chemical Physics · 2025
of 0.935 and an RMSE of 0.084, outperforming other models and a previously developed graph neural network. Feature importance analysis revealed key descriptors, including NBO charges and HOMO energies, driving the predictions. Applicability domain (AD) analysis identified outliers and compounds outside the AD, ensuring model reliability. This work highlights the potential of ML in predicting Hammett constants, offering a robust tool for chemical reactivity analysis and molecular design.