Improving the Stability and Transferability of Effective ADMET Models by Adding Quantum Mechanical Descriptors.

This study examines the effect of combining quantum mechanical (QM) descriptors with traditional structural descriptors in predicting ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties for molecular drug candidates. We focus on four end points: HLM (human liver microsomes) stability, permeability, solubility, and hERG (human ether-a-go-go-related gene) inhibition. First, we studied the prediction of molecules in the same chemical space as the training data. Our results show that incorporating QM descriptors enhances prediction accuracy across all end points. We find that the most significant improvement in permeability is achieved, with the QM dispersion energy proving to be the second most important feature. In general, feature importance analysis revealed that QM descriptors are ranked among the top five features, providing more comprehensive insights than structural descriptors alone. Next, we studied predicting molecules in chemical spaces distinct from the training data. Here, QM descriptors exhibited superior extrapolation capabilities, particularly for permeability and hERG data sets. In an extrapolation scenario where the training data set size was systematically reduced, QM descriptors maintained higher stability than structural descriptors in prediction. A hybrid approach combining all QM descriptors with key structural descriptors improved predictive accuracy, reduced variation in results, and yielded greater robustness in small data sets. This work highlights the value of QM descriptors for ADMET predictions, especially in challenging extrapolation tasks and scenarios with limited training data.