In the context of text-based models, sophisticated text-based chemical inputs have been developed such as the Simplified Molecular Input Line Entry System (SMILES), the Self-Referencing Embedded Strings (SELFIES), and SMILES arbitrary-target specification (SMARTS). Though convolutional neural networks have been tried successfully on SMILESbased text for the detection of chemical motifs and on prediction of drug activity, the majority of so-called "linear graph models" have been fitted using recurrent neural networks (RNNs) due to their capacity of holding short-and long-term informaa University of New Brunswick, Department of Chemistry. 30 Dineen Dr, Fredericton, Canada. E-mail: aelsamma@unb.ca b University of New Brunswick, Department of Mathematics and Statistics. 30 Dineen Dr, Fredericton, Canada. E-mail: stijn.debaerdemacker@unb.ca tion about text. For example, SMILES2Vec is a deep RNN that learns important features from SMILES strings to predict toxicity, activity, solubility and solvation energy of chemical compounds. Text-based models also facilitate the design of generative architectures that predict the result of chemical reactions, generating the product strings from a reactant string input, or generating molecules of a desired property. Graph models, on the other hand, are based on the raw visual representation of a molecule as a collection of atoms in three-dimensional coordinate space. Generally speaking, the coordinates of the atoms serve as inputs to such models and the output is the target chemical property under investigation, often on energy. Permutational-and symmetry-invariant graph models have been designed successfully for the prediction of electronic energy within chemical accuracy. Notably, such graph-based methods in chemistry share important properties with text-based recurrent and transformer models making it instructive to examine the connection between these seemingly different approaches.