An unlabelled dataset of approximately 100 million SMILES strings was used to pre-train the models. These molecules were randomly selected from roughly 1.5 billion molecules available from the publicly accessible ZINC-15 dataset with the following constraints: reactivity set to reactive, purchasability set to annotated, molecular weight ≤ 500 Daltons and LogP ≤ 5. Train, validation and test splits were then randomly assigned, with training data taking 99% and validation and testing each assigned 0.5% of the 100 million molecules.