Interaction-Aligned Surface Representations for Generalizable Blind Docking

Recent advances in machine learning have substantially improved protein–ligand docking, yet blind docking still generalizes poorly to unfamiliar receptors and binding environments. We argue that part of this difficulty arises from a mismatch between structural representation and interaction-level invariance: global structural novelty need not imply interaction novelty, as substantial receptor variation can leave the geometry and chemistry of relevant binding interfaces largely unchanged. Under limited supervision, however, Euclidean geometric encoders must infer such invariances implicitly and may entangle transferable interaction patterns with receptor-specific structural variation. We introduce SADock, a geometric diffusion framework that addresses this mismatch by explicitly representing both proteins and ligands through molecular surfaces, the natural boundaries on which molecular recognition occurs. Surface geometry and physicochemical fields expose interaction-facing patterns more directly, while a hierarchical heterogeneous graph integrates surface representations with residue- and atom-level context across multiple spatial scales for receptor-wide search and fine-grained pose modeling. We further establish conditions under which boundary-preserving structural changes retain binding-basin preferences, providing a theoretical characterization of why surface-aligned representations can remain transferable under structural variation. Across multiple benchmarks, SADock achieves higher docking success rates and lower pose RMSD. On challenging DockGen, our method improves Top-1/Top-5 docking success rates from 22.8%/29.6% for the strongest evaluated baseline to 27.0%/38.6%, despite using less data augmentation and without inference tuning tricks. Further analysis reveals substantial gains in pocket localization, a key bottleneck in generalizable blind docking, supporting surface-aware interaction modeling as a principled inductive bias under structural distribution shift.