In the future, the impurity prediction model may be improved using a model ensemble approach. Different model architectures, such as the transformer-based models in IBM-RXN , where the model learns to map different character tokens for reaction prediction, can help in obtaining a larger and potentially more diverse set of impurities than the graph-based approach in ASKCOS. In addition to that, novel architectures that combine the attention mechanism of transformer models with the inherent graph representations of molecules, such as those presented by Mao and co-workers , and Tu and Coley , can be adapted for impurity prediction. The key consideration here is using a similar training dataset, assuming that a large-scale dataset is infeasible in the short term, and leveraging different model architectures to identify different products.