Assessing the performance of quantum-mechanical descriptors in physicochemical and biological property prediction
Alejandra Hinostroza Caldas, А. О. Кокорин, Alexandre Tkatchenko, Leonardo Rafael Medrano Sandonas · Digital Discovery · 2026
and the MoleculeNet benchmark datasets. Moreover, a SHapley Additive exPlanations (SHAP) analysis of the toxicity and lipophilicity predictive models reveals that molecular orbital energies and DFTB energy components are among the most influential electronic features. Hence, our work underscores the importance of incorporating QM descriptors to enhance both the accuracy and interpretability of ML models for predicting multiple properties relevant to pharmaceutical and biological applications.