{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"submitter":["Brinkmann H"],"funding":["European Research Council","Dutch Research Council (NWO)"],"pubmed_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 <i>de novo</i> molecule design. Herein, we investigated whether rethinking SMILES augmentation techniques could further enhance the quality of <i>de novo</i> 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. <i>Via</i> systematic analysis, our results show"],"journal":["Digital discovery"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12409607"],"repository":["biostudies-literature"],"pubmed_title":["Going beyond SMILES enumeration for data augmentation in generative drug discovery."],"pmcid":["PMC12409607"],"funding_grant_id":["101077879","ReMINDER 101077879","11527","Starting Grant"],"pubmed_authors":["Brinkmann H","Ter Steege H","Argante A","Grisoni F"],"additional_accession":[]},"is_claimable":false,"name":"Going beyond SMILES enumeration for data augmentation in generative drug discovery.","description":"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 <i>de novo</i> molecule design. Herein, we investigated whether rethinking SMILES augmentation techniques could further enhance the quality of <i>de novo</i> 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. <i>Via</i> systematic analysis, our results show","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Aug","modification":"2026-06-01T14:00:15.624Z","creation":"2026-04-08T13:16:06.455Z"},"accession":"S-EPMC12409607","cross_references":{"pubmed":["40917333"],"doi":["10.1039/d5dd00028a"]}}