Toward interpretable foundation models for molecular biology.

Design elements and a checklist enable the development of interpretable foundation models in molecular biology that are as powerful as they are understandable.

• Why it matters: Interpretability is crucial in molecular biology machine learning models to ensure that predictions can be understood and trusted. Addressing this need helps bridge the gap between complex models and practical biological insights.
• What they did: The authors propose specific design elements and a checklist aimed at creating next-generation foundation models that prioritize interpretability alongside predictive power in molecular biology.
• The result: This approach facilitates the development of models that are both highly effective and transparent, supporting more reliable and insightful applications in molecular biology research.