Recently, state-of-the-art data-driven methods have gravitated towards black-box models with limited interpretability or transparency. As a result, it is harder to understand how or why certain products are predicted, introducing ambiguity in diagnosing erroneous outcomes. This is further exacerbated by the relatively little focus given to the underlying data used to train and test models, which can contain biases, and lack diversity. At present, it can be challenging to pinpoint where the issues lie: the quality of the underlying data, or the type of model employed, or both. Although the field is rapidly progressing to address these drawbacks and challenges, The 14-step workflow is fully automated and was implemented in Python using RDKit, a popular open-source cheminformatics library that facilitates reaction representation and manipulation. The workflow was run on a Linux (Ubuntu V 16.04.6 LTS) 32-core server using Ray, an open-source Python library for distributed computing. To more clearly explain the workflow presented in Figure , an illustrative case study is outlined here, based on paracetamol (acetaminophen) synthesis, Scheme 1. This is a widely practiced reaction that involves acetylation of 4-aminophenol (1) with acetic anhydride (2) to produce paracetamol (3) and acetic acid (4). Based on impurity studies, and shown in Scheme 1b, a possible impurity or by-product is 4-acetamidophenyl acetate (5), which is obtained when 3 is further acetylated by 2 in an overreaction. The proposed workflow should be able to predict this impurity and is explained in more detail in the following sections.