Toward the Explainability of Protein Language Models

Black box problem limits scientific trust: Protein language models achieve impressive results in tasks like structure prediction and enzyme design, but operate as "black boxes" with unclear underlying principles, making it difficult for scientists to understand why they make specific predictions or recommendations.

Five potential roles for explainability: The paper identifies five ways explainability can enhance protein research—Evaluator, Multitasker, Engineer, Coach, and Teacher—though only the Evaluator role (assessing model performance) is currently widely adopted, suggesting significant untapped potential for responsible AI implementation.

Explainability can address critical risks: The authors highlight that explainability methods are essential for identifying and mitigating security vulnerabilities, trustworthiness issues, and algorithmic bias in protein AI systems—concerns that directly impact the safety and reliability of AI-driven biological research.

Multiple levels of explanation needed: The paper categorizes explainability across four information contexts (training data, input prompts, model architecture, and input-output relationships), indicating that responsible AI requires examining these systems from multiple angles rather than relying on single explanation methods.

Community infrastructure is lacking: The authors call for benchmarks, open-source tools, domain-specific visualizations, and experimental validation ("wet-lab characterization"), suggesting that responsible deployment of protein AI requires building shared resources and standards across the research community.