By combining these two facets of scaffold analysis, token deletion results the most promising approach for exploring both novel chemical scaffolds and decorations of recurring scaffolds. Atom maskingwhile still producing good values of novelty and diversity -is better suited to decorating recurring finetuning scaffolds). Like enumeration, bioisosteric substitution is a valuable option for both scaffold decoration and scaffold exploration, with a dataset-dependent performance. These results confirm the value of optimizing the chosen SMILES augmentation strategies when utilizing generative deep learning for chemical space exploration and/or molecule optimization. Table . Scaffold Diversity and Novelty. Metrics are measured after fine-tuning on bioactive molecules for three targets (PPAR, PIM1, JAK2), using 10 and 100 molecules selected with (a) high similarity, and (b) low similarity. Scaffold diversity and novelty relative to the fine-tuning sets (FT) and pre-training set (PT) are reported as the mean ± standard deviation for 10 fine-tuning molecules.