However, one disadvantage of the DEKOIS2.0 library is that it was designed to evaluate docking algorithms. Hence, ML models trained by ligand-based descriptors could have artificially high classification performance, which cannot be generalized in a real VS setting where millions of compounds are screened for bioactivity. As a result, our null ML models, which are trained with all other features but the PM6/COSMO terms, have slightly lower AUC-ROC than the SQM-ML model which includes the PM6/COSMO structure-based scoring.