Machine learning methods to classify or predict the properties of molecules have become omnipresent tools in chemical and biological research. Classification tasks include categorising molecules into toxic and non-toxic, proteinbinding and non-binding, or otherwise pharmacological active or inactive compounds. Meanwhile, regression tasks encompass predicting various physicochemical and pharmacological properties, such as solubility and lipophilicity, protein-ligand binding affinity, or even quantum chemical properties. With the rise of deep learning during the past decade, molecular classification and property prediction have increasingly been carried out by ever-larger models with mixed results, as in tasks such as pharmacokinetic property prediction, where data remains scarce, deep learning methods have yet to perform significantly better than ensemble methods . Across all machine learning approaches, the most utilised methods are fingerprint-, SMILES-, and graph-based approaches, where molecular feature vectors, text representations of molecules, and molecular graphs, respectively, are used as the input of the respective class of models (MLPs, transformers, and GNNs) . Even though the text-based SMILES encoding of a molecule is often called a 1D representation (as opposed to the "2D" molecular graph and the 3D molecular structure), a SMILES string contains all information of its respective molecular graph, as it is constructed by traversing said graph using a depth-first search (DFS) algorithm . Furthermore, it also contains implicit and explicit information on the 3D structure of the molecule, as molecular structure is tied to molecular topology, and molecular chirality is often directly defined using the SMILES notation.