Other molecular representations often used for deep learning, like molecular graphs or molecular descriptors are less suited to data augmentation. Molecular graphs, for instance, encode molecular topology (atoms and bonds) and properties (atom properties and/or coordinates, and bond properties) in a permutation-invariant manner. In other words, every molecule (given a set of atom and bond properties to capture) maps to a unique molecular graph, rendering a 'SMILES-like' augmentation impossible. Similarly, molecular descriptors map one-to-one with the molecular representation they are computed from, rendering permutation-based augmentation unfeasible. A useful, albeit less explored, approach to circumvent this limitation is by considering 3D conformations and performing 'conformer-based augmentation'. Here, the same molecule is described by several, different 3D conformations, which enables data augmentation with molecular graphs and/or molecular descriptors that capture 3D information . How to aggregate information on different conformations is, however, still far from trivial, and several approaches can be explored . To date, the benefits of using multiple conformations are not fully evident , , and the performance might be affected by the chosen conformer generator . Finally, 3D-based approaches do not necessarily outperform more well-established ones that consider only 2D molecular information (e.g., ).