For impurity predictions, we are limited by the accuracy of the forward reaction prediction model being used. In our case, using ASKCOS, we are implicitly limited by how well the prediction model functions . Since ASKCOS is focused on predicting major products alone, we tried to extend the predictions by increasing probability thresholds and top-n predictions. However, these changes result in filtering out predicted molecules, showing a larger number of molecules, but not changing the predictions themselves. Thus, if ASKCOS was not able to predict an impurity amongst the tens of molecules, then it would not be able to do so unless the training dataset is improved. Further, the ranking of products for the impurity module in ASKCOS is based on structural similarity to the major product. This metric might not be an accurate depiction of a lot of impurities. A number of hard-to-identify impurities arise from reactions with impurities in the starting materials. With limited information about the low-level impurities in the starting materials, it is nearly impossible to predict such impurities.