Protein language model embeddings improve HIV drug resistance prediction: a comprehensive benchmark with attention-based interpretability.

Abstract excerpt

MOTIVATION: Accurate prediction of HIV drug resistance from viral sequences is critical for optimizing antiretroviral therapy. Traditional machine-learning approaches using binary mutation encoding achieve strong accuracy but may fail to capture epistatic interactions and structural features relevant to resistance mechanisms. Protein language models (PLMs) offer learned representations encoding evolutionary and...

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