As a result of the current processes, the aforementioned approaches may miss complexities seen in the real world, such as impurities that propagate from prior steps, or ones that are a product of reactions with impurities in starting materials, which are rarely of 100% purity. Moreover, due to the implicit bias of the majority product in the training dataset, they might miss low-level impurities. Unfortunately, these low-level impurities are critical to the drug substance development process and need to be identified and controlled. Thus, it is important to develop computational methods that can address the challenges around impurity prediction and propagation, and help in accelerating the chemical process development in the pharmaceutical industry.