Protein language models' allergen explanations lack biological grounding

A new study published on arXiv questions the biological relevance of explanations provided by protein language models used in allergenicity classification. While models like ESM-2 and DeepPlantAllergy demonstrate strong protein-level prediction accuracy, their residue-level attribution signals do not significantly align with annotated allergen epitopes. The research suggests these models may rely on general sequence features rather than specific immunological mechanisms, cautioning against interpreting their explanations as direct immunological insights for safety screening or hypoallergen design without rigorous validation. AI

IMPACT Challenges the interpretability of protein language models for safety-critical applications like allergen screening.

RANK_REASON Research paper published on arXiv detailing limitations of AI models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• allergen epitopes
• arXiv
• Deep allergenicity classifiers
• DeepPlantAllergy
• ESM-2
• Hugging Face
• Integrated Gradients
• protein language models

• paper
• safety

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