AbPilot: Steering Diffusion Co-folding Models for Antibody Design via End-to-End Differentiation

Modern all-atom co-folding models have sharply improved antibody–antigen structure prediction, yet their stochastic diffusion structure decoders make direct gradient-based antibody design difficult. Existing methods therefore either rely on simpler predictors or bypass the diffusion process, underexploiting the full capacity of co-folding models. We present AbPilot, which enables end-to-end differentiation through the native diffusion sampling process and propagates atomic-level structural and confidence objectives back to antibody sequence for steered generation. The gradient reaches the coordinates the model actually emits, so the interface can be controlled at atomic resolution. Using adjoint-state recursion, AbPilot reduces activation memory to O(1) with respect to diffusion depth, and further introduces orthogonal rectification to mitigate predictor hacking using an external structural verification as a search signal. On a new 12-target de novo antibody design benchmark, AbPilot achieves in silico success rates by more than an order of magnitude over the strongest baseline for both VHH and scFv design. Finally, prospective VHH design against mICAM-1, a target with no previously reported VHH binder, yields nanomolar binders, demonstrating that the proposed end-to-end differentiation can turn modern diffusion co-folding models into effective antibody design engines.