Scoring Performance Comparison.

The models were trained on PDBbind Refined Set (version V2020) with parameters tuned via the Core Set (version V2020), and tested on two sets from the CSAR source. State-of-the-art deep learning models (ACNN, OnionNet, KDEEP and GraphBAR) for scoring the protein-ligand complexes were realized, to comprehensively evaluate the proposed AGIMA-Score models. For GraphBAR, different graph adjacency schemes (2 or 3 adjacency matrices) were adopted for model construction. For AGIMA-Score, different node features (separately referring to Pafnucy, KDEEP and GraphBAR) and adjacency schemes (2 adjacency matrices or single adjacency matrix) were considered for model investigation. By default, 2 adjacency matrices (generated by intermolecular atomic contacts within and those within ) were adopted in the graph learning by AGIMA-Score. Best performance in terms of PC and RMSE were underlined for the state-of-the-art methods and the proposed AGIMA-Score models.