Additionally, we also used a number of representation learning methods implemented within KGCNN, which were adapted to be used in the OCHEM (Table ). These methods do not need descriptors but learn internal features based on representation of molecules as SMILES or graphs. The differences in methods appear due to various attributes of nodes and vertices of graphs (i.e., atoms and bonds for molecules), different training algorithms, model pretraining and/or use of 3D information, which was generated internally using RDKit 24 from SMILES. We also used the Transformer CNN, which is based on analysis of internal latent representation learned during SMILES canonisation of 1.7M molecules from the ChEMBL database.