  Machine learning (ML) has demonstrated significant promise in accelerating
drug design. Active ML-guided optimization of therapeutic molecules typically
relies on a surrogate model predicting the target property of interest. The
model predictions are used to determine which designs to evaluate in the lab,
and the model is updated on the new measurements to inform the next cycle of
decisions. A key challenge is that the experimental feedback from each cycle
inspires changes in the candidate proposal or experimental protocol for the
next cycle, which lead to distribution shifts. To promote robustness to these
shifts, we must account for them explicitly in the model training. We apply
domain generalization (DG) methods to classify the stability of interactions
between an antibody and antigen across five domains defined by design cycles.
Our results suggest that foundational models and ensembling improve predictive
performance on out-of-distribution domains. We publicly release our codebase
extending the DG benchmark ``DomainBed,'' and the associated dataset of
antibody sequences and structures emulating distribution shifts across design
cycles.
