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bioRxiv · 10.1101/2025.08.05.668698

Benchmarking antigen-aware inverse folding methods for antibody design.

Janusz, B.·Chomicz, D.·Wrobel, S.·Dudzic, P.·Polasa, A.·Martin, K.·Darnell, S.·Comeau, S. R.·Krawczyk, K.
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Abstract

Computational antibody design has seen many recent advances pioneered via the use of language models and advanced structure prediction tools. Developing a de novo antibody against a specific antigen requires structural awareness that most language models lack. A prominent class of machine learning methods combining the best of language model and structural worlds is inverse folding. This approach aims to predict a sequence that would fit a given structure. Such methods are now increasingly used to predict alternate sequences given a structure of a binder. It is known that, just like language models, such methods have certain predictive power in identifying binders. Here we performed a set of tests to reveal where, if at all, such methods provide value in the realistic setting of antibody discovery.

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Janusz, B., Chomicz, D., Wrobel, S., Dudzic, P., Polasa, A., Martin, K., Darnell, S., Comeau, S. R., Krawczyk, K.. 2025-08-07. Benchmarking antigen-aware inverse folding methods for antibody design.. https://doi.org/10.1101/2025.08.05.668698

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