BLOSUM Is All You Learn - Generative Antibody Models Reflect Evolutionary Priors

Abstract

Generative models have emerged as powerful tools for antibody sequence design, with recent studies demonstrating that log-likelihood scores from these models can correlate with binding affinity and potentially serve as effective ranking metrics. This raises a fundamental question: why should log-likelihood scores from generative models correlate with binding affinity? In this work, we investigate the biochemical basis of these model-derived log-likelihoods by comparing them with classical evolutionary similarity metrics. We find that BLOSUM similarity scores between designed and parental antibody sequences correlate strongly with measured binding affinity--on par with the predictive performance of a state-of-the-art diffusion-based generative model. Moreover, these BLOSUM scores also align closely with log-likelihoods from multiple generative models, suggesting that such models may be implicitly learning evolutionary priors encoded in substitution matrices. When computed with respect to a known binder, both BLO-SUM scores and log-likelihoods act as approximate measures of sequence distance from that reference. As this distance increases, the likelihood of a candidate being a binder decreases, explaining the observed correlation between these scores and binding affinity. In contrast, similarity scores based on position weight matrices (PWMs) and position-specific scoring matrices (PSSMs), which do not rely on knowledge of the parental sequence, show weaker and less consistent alignment with binding affinity, with performance depending on the background sequence data. Additionally, using consensus sequences in place of parental sequences to compute BLOSUM scores largely eliminates the observed correlation with affinity, underscoring the context-specific nature of the correlations. These findings highlight the potential of interpretable, evolution-inspired metrics to complement generative modeling in anti-body design, offering insights into both model behavior and biological relevance.