AbDist: a lightweight, distance-based model for antibody affinity prediction as an interpretable benchmark for machine learning models.

Many complex models for antibody affinity prediction have been developed and successfully deployed. Recent results for T-cell receptor epitope prediction have shown, that even simple distance-based models can achieve a similar performance while requiring less parameters, being more easily interpretable and faster to compute. Encouraged by these results AbDist, a new distance-based model, was developed for antibody affinity prediction. It uses fragments around mutation sites to calculate distances between antibody sequences, demonstrating that a local environment alone suffices as an effective featurization. AbDist was used to perform classification and regression tasks on multiple disjunct public datasets. Its performance matches state-of-the-art machine-learning (ML) models. AbDist is interpretable, computationally efficient, and well suited for data-sparse, early-stage antibody engineering workflows, while sharing the limited out-of-distribution generalization common to current models. AbDist is available as an open-source, publicly accessible tool.