Epitope-Conditioned Antibody Generation
Mechanism Figures
These figures summarize the scientific story behind the viewer: REACH-Ab first defines the antibody design task at the complex level, then maintains candidate epitope-paratope relations as dynamic states, and finally feeds selected relation states back into local message passing.
Framework Overview
The framework initializes masked CDR residues from an antibody-antigen complex, builds global and local graphs, and iteratively couples SE(3) message passing with a local Dynamic Interface Edge Field. The key idea is that candidate interface edges are explored, updated, and selected before they guide local coordinate refinement and sequence generation.
Core Concept
REACH-Ab maintains many possible epitope-paratope contacts instead of committing only to current geometric neighbors. During refinement, half-edge evidence and lifecycle control let some relations stabilize, some compete, and some die out. This is the mechanism that reduces epitope guidance lag: antigen-facing contact hypotheses remain available before the paratope geometry is fully reliable.
Implementation Flow
At each refinement step, residue states and anchors construct a directed candidate edge pool. Pair-level features are updated through source-side and destination-side half-edge probing, then fused into persistent edge memory. Distance, occupancy, birth, death, persistence, and uncertainty heads produce an active edge score; selected edge states then become attributes in local message passing and are carried to the next refinement step.
Edge-Field-Guided Generation
The main-paper mechanism claim is temporal: REACH-Ab should expose useful epitope-paratope contact hypotheses before the final CDR geometry is settled. These 57 trajectories compare a geometry-kNN proxy with the REACH-Ab edge field for the same benchmark cases shown in the PDB viewer.
The left panel reconstructs edges after provisional node geometry appears. The right panel shows REACH-Ab maintaining and activating candidate edge states during refinement, so contact evidence can participate while the paratope is still being formed.
Paper to Code
This section merges the main-paper REACH-Ab method with the supplementary Model Architecture and Implementation Details and Theoretical Analysis. Each formula below is clickable: selecting it opens the concrete PyTorch implementation used by the current codebase.
REACH-Ab starts from a broad directed epitope-paratope candidate pool, encodes pair geometry and endpoint states, injects two-sided half-edge evidence, stores candidate-level memory, predicts lifecycle and uncertainty signals, then selects a sparse active interface graph for local message passing.
The supplementary analysis explains why this matters: a broad candidate space preserves early contact accessibility, vector edge states are more expressive than scalar scores, half-edge probing generalizes one-sided scoring, and memory allows delayed activation from repeated weak evidence.