Model choice and description. The crux of the AI approaches we use is to translate molecular structures into a high-dimensional vector space, in which similarity between compounds is reflected by the proximity of the corresponding points/vectors. We will refer to this data transformation as either "vectorization" or "embedding". In particular, we employ distance metrics based on two vectorization techniques: (1) the linguistics-inspired Mol2Vec embedding and (2) Estimated Shape Representation (ESR) developed here as an extension to the model originally created by Skalic and co-workers .