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 - wherein multiple valid SMILES strings are used to represent the same molecules - has become particularly beneficial to improve the quality of de novo molecule design. Herein, we investigated 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 systematic analysis, our results show

SUBMITTER: Brinkmann H 

PROVIDER: S-EPMC12409607 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature