A dataset of SMILES was used to pre-train the generative model used in this work. This consisted of molecules extracted from ChEMBL28 which underwent further filtering and refinement. First, only molecules with a pChEMBL value greater than 6 from an assay with a confidence value of 8 or higher were considered, as a proxy for more developed chemistry resulting in more medicinally interesting chemistry. These molecules were further standardized, neutralized, and filtered to ensure molecules had a predicted logP less than or equal to 4.5, rotatable bond count less than or equal to 7, molecular weight in the range 150 to 650 Da and only contain atoms belonging to the following set A ∈ {C, S, O, N, H, F, Cl, Br}. Any molecules violating MOSES structural alerts were removed . Finally, molecules were clustered based on scaffold similarity using ECFP4 fingerprints at a threshold of 0.8 Tanimoto similarity and only the centroids were carried forward into the resulting dataset of 189,238 unique molecules. This under-sampling approach was used to even out the chemical space. During training, 10-fold restricted randomisation of the SMILES representation was conducted to augment the training dataset .