In terms of performance, SQM-ML achieved stable predictive accuracy throughout all tested targets fluctuating around 0.73 AUC-ROC, while PM6/COSMO and Glide demonstrated vigorously target-specific performance. To provide insights into the reasons why SQM-ML couldn't outperform PM6/COSMO in TP, GR, and DHFR datasets, we computed the feature importances. First and foremost, it is clear from the 3D UMAP projections of PLEC fingerprints (Figure ) that in these 3 datasets the interactions between ligands and the receptor follow a specific pattern, which is dissimilar to the interactions between decoys and receptor. Therefore a purely non-covalent interaction-based scoring function like PM6/COSMO has an advantage in these sets. By diving deeper into the mechanism of scoring, we discovered that the SQM-ML model used for validation on the DHFR dataset gave the greatest emphasis to PM6/COSMO interaction energy (Eint), number of aromatic and single bonds (bondType_AROMATIC, bondType_SINGLE), molecular weight (MW) and average molecular weight (aMW) (Figure ). The badly scored compounds, which are illustrated in Figure are large, contain many aromatic rings and single bonds, and many polar/charged groups that contribute to lower PM6/COSMO interaction energies. However, according to the retrospective calculation of feature importances, a good ML model for the DHFR ligand decoy set should give less emphasis to Eint, and minimal to no emphasis to bondType_AROMATIC, bondType_SINGLE, MW, and aMW (Figure ). It is therefore clear why SQM-ML is weaker than PM6/COSMO interaction energy in DHFR. In the GR set, the most important feature of the SQM-ML model was bondType_AROMATIC, followed by Eint and bondType_SINGLE with similar importance, and after them MW and AMW. This is in stark contrast with the optimum weighting of features for this set, according to which bondType_AROMATIC along with MW have negligible importance, bond-Type_SINGLE half of the importance of Eint, and AMW less than 1/3 of the importance of Eint. It is therefore not surprising that the 20 worst-scoring molecules shown in Figure are large and comprise many aromatic systems.