Generalizing Equivariant Neural Networks for Protein–Ligand Binding by Comparing Atomic Environments

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 what additional inductive biases should be built into an equivariant message passing network (MPNN) trained from scratch, and focus on how the network can use the unbound state of a complex. In a basic equivariant MPNN, adding the unbound state as a second pooled input does not improve accuracy. Because the two states contain the same atoms, their embeddings can instead be combined at each atom, at each intermolecular edge and over the pocket, through operations we refer to as paired readouts. The resulting model, PEAR, is to our knowledge the most accurate model on CASF-2016 when trained on CleanSplit, including models with pretrained embeddings. Furthermore, a variant of PEAR, PEAR_LBA, matches the best reported accuracy on ATOM3D LBA at 30% sequence identity. On seven held-out pocket clusters, a residue-scale variant, PEAR_residue, has the highest average correlation of any single model after finetuning on 25 complexes of each target. When developing PEAR, we were additionally inspired by the fact that binding affinity is an ensemble average over configurations, so that a model should not lean too heavily on the exact coordinates of its input structure. We find that PEAR is the most accurate atom-scale model under coordinate noise perturbation, and that PEAR_residue degrades about half as much under the same perturbation.