Deep learning (DL) and machine learning (ML) have been revolutionising the computational chemistry and computer-aided drug discovery (CADD) landscape over the past few years. Substantial predictive improvements are described using DL methods for a diverse range of molecular modelling tasks, such as molecular docking and virtual screening , materials discovery , property prediction , lead optimisation and generative chemistry . Indeed, the way chemical data is represented at the molecular level can significantly influence the performance and the choice of ML/DL algorithms . Typical molecular representations for ML/DL applications include string-based (e.g., SMILES and SELFIES), bitstring fingerprints, graphs, and voxel grids .