Our structure-based (SB) model uses the features of the Random Forest-based scoring function RF-Score v3 . Six of these features are the same six terms used by the AutoDock Vina scoring function (five empirical force-field-like potentials derived from the interactions between protein and ligand atoms, and the number of rotatable bonds in the ligand). The remaining 36 features are the counts of pairwise interactions between protein and ligand atoms within 12 Å of each other, for example, the number of protein carbon-ligand nitrogen pairs. Four elements (C, N, O, and S) are considered in the protein and nine elements (C, N, O, F, P, S, Cl, Br, and I) in the ligand. These features were calculated using the Open Drug Discovery Toolkit version 0.6. For the hybrid (HB) model, we used the features of both the ligand-based and structurebased models as inputs and again trained Random Forest models using the training set described above.