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arXiv · 2512.06592

On fine-tuning Boltz-2 for protein-protein affinity prediction

James King·Lewis Cornwall·Andrei Cristian Nica·James Day·Aaron Sim·Neil Dalchau·Lilly Wollman·Joshua Meyers
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Abstract

Accurate prediction of protein-protein binding affinity is vital for understanding molecular interactions and designing therapeutics. We adapt Boltz-2, a state-of-the-art structure-based protein-ligand affinity predictor, for protein-protein affinity regression and evaluate it on two datasets, TCR3d and PPB-affinity. Despite high structural accuracy, Boltz-2-PPI underperforms relative to sequence-based alternatives in both small- and larger-scale data regimes. Combining embeddings from Boltz-2-PPI with sequence-based embeddings yields complementary improvements, particularly for weaker sequence models, suggesting different signals are learned by sequence- and structure-based models. Our results echo known biases associated with training with structural data and suggest that current structure-based representations are not primed for performant affinity prediction.

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James King, Lewis Cornwall, Andrei Cristian Nica, James Day, Aaron Sim, Neil Dalchau, Lilly Wollman, Joshua Meyers. 2025-12-06. On fine-tuning Boltz-2 for protein-protein affinity prediction. https://arxiv.org/abs/2512.06592

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