AlphaBind, a domain-specific model to predict and optimize antibody-antigen binding affinity.

ABSTRACT: Antibodies are versatile therapeutic molecules that use combinatorial sequence diversity to cover a vast fitness landscape. Designing optimal antibody sequences, however, remains a major challenge. Recent advances in deep learning provide opportunities to address this challenge by learning sequence-function relationships to accurately predict fitness landscapes. These models enable efficient in silico prescreening and optimization of antibody candidates. By focusing experimental efforts on the most promising candidates guided by deep learning predictions, antibodies with optimal properties can be designed more quickly and effectively. Here we present AlphaBind, a domain-specific model that uses protein language model embeddings and pre-training on millions of quantitative laboratory

SUBMITTER: Agarwal AA 

PROVIDER: S-EPMC12296056 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature