Assessing the performance of quantum-mechanical descriptors in physicochemical and biological property prediction
Abstract
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.
Fields of Study & Subjects
Computational Drug Discovery Methods
Machine Learning in Materials Science
Protein Structure and Dynamics
Publication details
- Journal / source
- Digital Discovery
- Publisher
- Royal Society of Chemistry (RSC)
- Volume
- 5
- Issue
- 2
- Pages
- 803–818
- Publication date
- 1 January 2026
- Language
- EN