RNNs were trained using either a modification of the MOSES dataset or the GuacaMol dataset. Firstly, the MOSES dataset is derived from ZINC15 clean leads and contains a library of 'drug-like' small organic molecules. It is designed to benchmark generative model de novo molecule generation. The MOSES dataset applies several filters during curation including: molecular weight between 250-350 Da; number of rotatable bonds not greater than 8; XlogP not greater than 3.5; no atoms besides C, N, S, O, F, Cl, Br, H; no cycles larger than 7 members; molecules adhering to custom medicinal chemistry and PAINS filters . In addition, charged species are removed; here however, we deviate from this curation by neutralising charged species and hence avoid a bias towards non-protonatable groups. To distinguish this from the original MOSES dataset, we refer to this as MOSES neutralized (MOSES n ) . This resulted in a training set of 2,454,087 molecules. The GuacaMol train dataset (1,273,104 molecules) is derived from ChEMBL24 and contains real molecules both in the 'drug-like' domain and others such as peptides and natural products. This dataset was designed to benchmark both generative model de novo molecule generation and subsequent objective optimization. The GuacaMol dataset applies the following filters during curation: salt removal; charge neutralization; molecules with SMILES strings < 100; no atoms besides H, B, C, N, O, F, Si, P, S, Cl, Se, Br, and I. Therefore, the GuacaMol dataset results in a training set with a much broader variety of chemotypes present than MOSES n .