Improving the generalization capabilities of equivariant message passing neural networks for binding affinity prediction

Structure-based models of protein–ligand binding affinity have long been benchmarked by training on PDBbind and testing on CASF-2016, a train/test split in which nearly half of the test complexes have close analogs in the training data. Many recent models developed on splits that remove these analogs rely on pretrained embeddings. We ask the complementary question of which inductive biases are worth building into an equivariant message passing network (MPNN) trained from scratch, and focus on how the network can use the unbound state of a complex, obtained by removing intermolecular edges, in a way that is advantageous to generalization. We find that, in a basic equivariant MPNN trained on PDBbind CleanSplit, adding the unbound state as a second pooled input does not improve accuracy. We notice that, because the two states contain the same atoms, their embeddings can be combined at each atom, at each intermolecular edge and over the pocket using a variety of operations. We refer to these operations as paired readouts, and perform experiments to find a combination of them that enhances binding affinity prediction. The resulting model, PEAR (Paired Embeddings of Atoms and Residues), is the most accurate of the compared models on CASF-2016, including those with pretrained embeddings, and removing any of its paired readouts lowers its accuracy. On ATOM3D LBA at 30% sequence identity, a variant of PEAR matches the best reported accuracy. On seven held-out pocket clusters, a residue-scale variant has the highest average correlation of any single model after finetuning on 25 complexes of each target, and finetuning improves each of our models on every cluster.