Machine learning models have become increasingly popular for risk assessment of chemical compounds. However, they are often considered 'black boxes' due to their lack of transparency. To increase confidence in these models, researchers proposed carefully identifying the areas of chemical space where these models are weak. They developed an innovative software tool for this purpose, and the results of this research approach have just been published.
