{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Zhang J"],"funding":["National Natural Science Foundation of China"],"pagination":["bbag039"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12908692"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["27(1)"],"pubmed_abstract":["Identifying drug-drug interactions (DDIs) is a critical task in pharmaceutical research and clinical applications, as these interactions can pose serious medical risks. Deep learning models, known for their ability to accurately predict DDIs, have become powerful tools for enhancing prediction accuracy and efficiency. However, many existing approaches fail to fully incorporate chemical information and lack interpretability when exploring DDI mechanisms. In this work, we propose TRACE, a transformer-based graph representation learning framework that integrates chemical knowledge into DDI prediction. Extensive experiments demonstrate that TRACE outperforms state-of-the-art baseline models under both in-distribution and out-of-distribution settings, highlighting its strong predictive performa"],"journal":["Briefings in bioinformatics"],"pubmed_title":["Transformer-based graphs for drug-drug interaction with chemical knowledge embedding."],"pmcid":["PMC12908692"],"funding_grant_id":["U23A20513"],"pubmed_authors":["Shao X","Fan X","Zhang J","Zhang X","Dai Y"],"additional_accession":[]},"is_claimable":false,"name":"Transformer-based graphs for drug-drug interaction with chemical knowledge embedding.","description":"Identifying drug-drug interactions (DDIs) is a critical task in pharmaceutical research and clinical applications, as these interactions can pose serious medical risks. Deep learning models, known for their ability to accurately predict DDIs, have become powerful tools for enhancing prediction accuracy and efficiency. However, many existing approaches fail to fully incorporate chemical information and lack interpretability when exploring DDI mechanisms. In this work, we propose TRACE, a transformer-based graph representation learning framework that integrates chemical knowledge into DDI prediction. Extensive experiments demonstrate that TRACE outperforms state-of-the-art baseline models under both in-distribution and out-of-distribution settings, highlighting its strong predictive performa","dates":{"release":"2026-01-01T00:00:00Z","publication":"2026 Jan","modification":"2026-07-16T05:07:28.211Z","creation":"2026-07-10T03:08:46.118Z"},"accession":"S-EPMC12908692","cross_references":{"pubmed":["41697921"],"doi":["10.1093/bib/bbag039"]}}