Toward interpretable foundation models for molecular biology.

Design elements and a checklist enable the development of interpretable foundation models for molecular biology that match the power of existing models.

• Why it matters: Interpretability is crucial in molecular biology machine learning models to ensure understanding and trust, addressing the gap between prediction accuracy and explanation.
• What they did: The authors propose incorporating prior knowledge and prioritizing interpretability during model design, offering specific guidelines for future models.
• The result: This approach facilitates the creation of models that are both highly predictive and transparent, advancing their utility in biological research and applications.