We also investigated why SQM-ML managed to classify the compounds in datasets SIRT2, SARS-HCoV, GBA, and HIV1RT satisfactory, while both Glide SP and PM6/COSMO acted like random classifiers. From the 3D UMAP projections of PLEC fingerprints, it is evident that in these 4 datasets both actives and decoys form similar types of non-covalent interactions with the receptor, hence the sheer structure-based SFs failed to separate them.