Kemal Sönmez

The paper introduces a context-aware framework integrating text and protein language models to predict multiple antibody properties without retraining, achieving high correlation across developability properties.

The paper presents a computational workflow for designing nanobody binders against a novel cancer target, achieving sub-nanomolar affinities without experimental structure or prior antibody data.