T3 is a broader task, and many tools help data scientists to find (dis-)similarities in prediction behavior, performance, training behavior, and interpretability of models to choose the most suitable model. The capabilities of these tools include comparison of models using performance metrics, model interpretability, or other architecture-specific measures. However, we did not find any interactive tool designed for chemistry tasks that combine visualization of performance metrics and model interpretability. Data scientists can use programming toolkits with analytical and visualization features to accomplish T3. However, this approach is limited because interactive and coordinated visualizations cannot be promptly used out of the box.