AbICL: In-Context Learning for Antigen-Specific Antibody Affinity Ranking
AbICL is proposed, an ICL framework for antigen-specific antibody affinity ranking that combines a pretrained structural encoder with a context ranking head and is trained with an episodic meta-training strategy that enables the model to leverage support demonstrations for test-time adaptation without gradient updates.