All template-based frameworks share the same advantage, that is their inherent interpretability. While the deep-learning architectures employed by the framework are not easily interpretable, the predicted output is, i.e. the template itself. In other words, once a template is chosen by the framework, the end-user can check for its precedent in the literature along with the proposed reaction mechanisms and conditions. This is why these models are often favoured by experimental and organic chemists.