ALLM-Ab: Active Learning-Driven Antibody Optimization Using Fine-Tuned Protein Language Models.

ABSTRACT: Antibody engineering requires a delicate balance between enhancing binding affinity and maintaining developability properties. In this study, we present ALLM-Ab (Active Learning with Language Models for Antibodies), a novel active learning framework that leverages fine-tuned protein language models to accelerate antibody sequence optimization. By employing parameter-efficient fine-tuning via low-rank adaptation, coupled with a learning-to-rank strategy, ALLM-Ab accurately assesses mutant fitness while efficiently generating candidate sequences through direct sampling from the model's probability distribution. Furthermore, by integrating a multiobjective optimization scheme incorporating antibody developability metrics, the framework ensures that optimized sequences retain therapeutic antib

SUBMITTER: Furui K 

PROVIDER: S-EPMC12606632 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature