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. Author manuscript; available in PMC: 2023 Jun 19.
Published in final edited form as: J Chem Inf Model. 2022 Jun 14;62(12):2923–2932. doi: 10.1021/acs.jcim.2c00127

Table 2.

Evaluation of MedusaGraph against Other Approaches in Terms of Classification Accuracy and AUCa

Accuracy
 AUC
Evaluation metric avg min max avg min max
Medusadock N/A N/A N/A 0.474 0.462 0.489
Atomnet 0.741 0.628 0.872 0.863 0.849 0.885
Medusanet 0.855 0.705 0.93 0.893 0.868 0.915
Autodock Vina N/A N/A N/A 0.615 0.592 0.636
Graph-DTI 0.895 0.836 0.953 0.906 0.876 0.933
Pose selection 0.914 0.855 0.954 0.892 0.866 0.923
Pose prediction+selection 0.958 0.940 0.981 0.960 0.943 0.985
a

We evaluate these approaches on the PDBbind test set. Note that the accuracies for MedusaDock and Autodock Vina are marked as N/A because the scoring function of MedusaDock and the affinity score of Autodock cannot be used to distinguish between a good pose and bad pose. It can only be used to compare the goodness of two poses.