GermRL: Alleviating The Germline Bias In Autoregressive Antibody Language Models Through Reinforcement Learning

Abstract excerpt

<h4>ABSTRACT</h4> Antibodies are powerful therapeutics whose antigen specificity arises from sequence diversity shaped during development. Recently, language models trained on large antibody repertoire datasets have enabled the generation and screening of novel candidates, but these models retain a strong germline bias. As AI adoption increases in therapeutic workflows, it is crucial to develop models that harnes...

Topics

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• Amino Acid Sequence
• Antibodies
• Computational Biology
• Germ-Line Mutation
• Monoclonal and Polyclonal Antibodies Research
• Mutation
• Selection, Genetic
• vaccines and immunoinformatics approaches

Identifiers and source

Literature Corpus work

5c641b31-35bf-54de-9130-917b7efa3937

DOI

10.64898/2026.06.08.730660