Neural networks in chemistry have gained tremendous traction in the past decade, carrying a broad range of applicability, from aiding drug and material discovery, to speeding up or bypassing the prediction of electronic structure properties. For each application, numerous approaches have been designed, including both graph- and text-based models where such techniques enjoy a varying degree of success.