Toward interpretable foundation models for molecular biology

Abstract

For machine learning models in molecular biology, explanation is as important as prediction. Encoding prior knowledge and treating interpretability as a first-class design objective accomplish both. We provide design elements and a checklist for the next generation of foundation models in molecular biology that are as interpretable as they are powerful.