Adapting Molecular Foundation Models for Macrocyclic Peptide Affinity Prediction via Robust Proxy Rehearsal

Adapting molecular foundation models to macrocyclic peptide affinity prediction requires improving target-domain performance without sacrificing their existing small-molecule capabilities. When the original pretraining corpus is unavailable, public affinity measurements can serve as a source proxy, but their protein-family composition and coverage may differ from the true source distribution. We study retention under this imperfect source information. We show that reducing absolute worst-family risk does not guarantee retention, whereas controlling degradation relative to the pretrained model characterizes worst-case retention over arbitrary family proportions under full coverage and matched within-family distributions. A conditional bound further isolates the effects of finite sampling, within-family mismatch, and missing coverage. Motivated by this analysis, we introduce Robust Proxy Rehearsal (RPR), which combines target supervision with reference-relative source penalties and adaptively emphasizes protein families that degrade during adaptation. We instantiate RPR with Boltz-2 and evaluate macrocycle adaptation, small-molecule retention, and transfer to two external peptide benchmarks. In our primary comparison, RPR improves over uniform replay, increasing Spearman $\rho$ from 0.508 to 0.611 on the source domain and from 0.216 to 0.312 on the target domain, while also improving both external benchmarks. Additional runs show that these gains vary across hyperparameters, delineating when reference-relative rehearsal can improve the adaptation–retention trade-off under an imperfect proxy.