Going beyond SMILES enumeration for data augmentation in generative drug discovery

Abstract

Data augmentation can alleviate the limitations of small molecular datasets for generative deep learning, by ‘artificially inflating’ the number of instances available for training. SMILES enumeration – whereby multiple valid SMILES strings are used to represent the same molecules – has resulted particularly beneficial to improve the quality of de novo molecule design. Here, we investigate whether rethinking SMILES augmentation techniques could further enhance the quality of de novo design. To this end, we introduce four novel approaches for SMILES augmentation, drawing inspiration from natural language processing and chemistry insights: (a) token deletion, (b) atom masking, (c) bioisosteric substitution, and (d) self-training. Via a systematic analysis, our results show the promise of considering additional strategies for SMILES augmentation. Every strategy showed distinct advantages, with atom masking resulting particularly promising to learn desirable physico-chemical properties in very low-data regimes. This new repertoire of SMILES augmentation strategies expands the available toolkit to design molecules with bespoke properties in low-data scenarios.