From memorization to generalization: Why physics will improve machine learning -based prediction of protein complexes

AlphaFold-like models have transformed monomer structure prediction yet reliable, generalizable prediction of protein–protein interactions (PPIs) remains challenging, particularly for antibody–antigen docking. These limitations often stem from reliance on pattern memorization under severe data scarcity. Here, we review the emerging transition toward physics-integrated machine learning to address these gaps. We categorize recent efforts to improve generalization into three complementary approaches: (I) enriching inputs with physics-based sampling (e.g., molecular dynamics/fast Fourier transform ensembles); (II) designing architectures with strict geometric inductive biases (e.g., SE(3)-equivariance); and (III) constraining the generative process using physical energy functions or potentials. While standard models often struggle on out-of-distribution targets, these hybrid strategies aim to enforce physical plausibility at different stages of the prediction pipeline, offering a path from memorization to true physical generalization.