AffinitySym: Sequence-based Interpretable Protein–Protein Binding Affinity Prediction

Protein–protein binding affinity, often expressed as a change in Gibbs free energy (ΔG), quantifies the stability of a complex. Most predictors require a three-dimensional structure and return a scalar without explaining the estimate. AffinitySym is a multimodal, sequence-first deep neural model with frozen ESM-3 and MINT encoders. Bidirectional cross-attention combines partner sequences with additional equivariant graph neural network (EGNN) and invariant point attention (IPA) features when a structure is supplied; learned-query attention conditions an autoregressive Transformer decoder that generates 16 executable symbolic formulas. Dual-path training lets the same checkpoint predict from partner sequences alone or from those sequences paired with a supplied structure, making AffinitySym the only compared method to support both inference modes. Formula-conditioned residue attributions identify amino acid residues that contribute to each prediction. We train on the protein–protein subset of PDBbind v2020 and evaluate on two test sets adopted from prior studies: Test Set 1 (82 complexes) and Test Set 2 (166 wild-type and mutant complexes from SKEMPI v2.0). On Test Set 1, the best-observed sequence-only formula achieves an MAE of 1.2982 kcal/mol and a PCC of 0.7034, compared with an MAE of 1.36 kcal/mol and a PCC of 0.629 for the strongest listed baseline. The same checkpoint also performs competitively on Test Set 2, while a separate checkpoint achieves an MAE of 0.9960 kcal/mol and a PCC of 0.8493. Detailed comparisons on both test sets and multi-seed results are presented in the main paper. Frozen encoders reduce training and gradient-memory costs, while local inference lets researchers keep sensitive protein data on their own systems. Residue-level attributions can help prioritize binding-critical residues for drug discovery. Code Availability (Anonymous): https://anonymous.4open.science/r/AffinitySymProject-D964