SlowGuess/NeurIPS_2023_OCR huggingface dataset:
hyperparameter from Yang et al. [42]. For the protein encoder, we set hidden layer dimension to be 256 and try encoder depth  $L \in \{1, 2, 3\}$  and distance threshold  $d \in \{5.0, 10.0\}$ . In the antibody case, we try encoder depth from  $L \in \{1, 2, 3\}$  and distance threshold  $d \in \{10.0, 20.0\}$ . In both cases, we select the hyperparameter that gives the best Pearson correlation on the validation set.
Docking protocol. For Autodock Vina, we use its default docking parameters with docking grid dimension of  $20\AA$ , grid interval of  $0.375\AA$ , and exhaustiveness of 32. For ZDOCK, we mark antibody CDR residues as ligand binding site and generate 2000 poses for each antibody-antigen pair. We re-score those 2000 poses by ZRANK2 and select the best candidate.
Additional results. We include additional results for the antibody-antigen binding task. We first compare our method with additional physi-c-based potentials implemented in the CCharPPI web server [22], including ZRANK [24], RosettaDock [1], PyDock [10], SIPPER [27], AP_PISA [39], CP_PIE [30], FIREDOCK, and FIREDOCK_AB [2]. As shown in Table B, the performance of these models are much lower than NERE.
We have also explored more protein encoder architecture as additional supervised learning baselines. We consider a 3D convolutional neural network (3D CNN) and a graph convolutional network (GCN) implemented in the Atom3D package [38]. For the GCN model, we extend its implementation to include ESM-2 residue embedding. We find that the performance of these two models are much worse than FANN when trained on 273 antibody-antigen complexes from SKEMPI. Therefore, we choose the FANN architecture as our default supervised model architecture for the rest of our analysis.
Figure 4: Correlation between predicted and experimental binding energy.