{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Guo X"],"funding":["National Natural Science Foundation of China (National Science Foundation of China)"],"pagination":["183"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11228022"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["9(1)"],"pubmed_abstract":["Helicobacter pylori (H. pylori) is currently recognized as the primary carcinogenic pathogen associated with gastric tumorigenesis, and its high prevalence and resistance make it difficult to tackle. A graph neural network-based deep learning model, employing different training sets of 13,638 molecules for pre-training and fine-tuning, was aided in predicting and exploring novel molecules against H. pylori. A positively predicted novel berberine derivative 8 with 3,13-disubstituted alkene exhibited a potency against all tested drug-susceptible and resistant H. pylori strains with minimum inhibitory concentrations (MICs) of 0.25-0.5 μg/mL. Pharmacokinetic studies demonstrated an ideal gastric retention of 8, with the stomach concentration significantly higher than its MIC at 24 h post dose."],"journal":["Signal transduction and targeted therapy"],"pubmed_title":["A deep learning-driven discovery of berberine derivatives as novel antibacterial against multidrug-resistant Helicobacter pylori."],"pmcid":["PMC11228022"],"funding_grant_id":["32141003","82003575","61972384"],"pubmed_authors":["Jiang J","Zeng Q","Xu M","Lu X","Guo X","Song D","Chen F","Wei W","You X","Zhao X","Fan T","Zhao L","Zhang Z","Zhang X","Pang J","Wang Y","Feng S"],"additional_accession":[]},"is_claimable":false,"name":"A deep learning-driven discovery of berberine derivatives as novel antibacterial against multidrug-resistant Helicobacter pylori.","description":"Helicobacter pylori (H. pylori) is currently recognized as the primary carcinogenic pathogen associated with gastric tumorigenesis, and its high prevalence and resistance make it difficult to tackle. A graph neural network-based deep learning model, employing different training sets of 13,638 molecules for pre-training and fine-tuning, was aided in predicting and exploring novel molecules against H. pylori. A positively predicted novel berberine derivative 8 with 3,13-disubstituted alkene exhibited a potency against all tested drug-susceptible and resistant H. pylori strains with minimum inhibitory concentrations (MICs) of 0.25-0.5 μg/mL. Pharmacokinetic studies demonstrated an ideal gastric retention of 8, with the stomach concentration significantly higher than its MIC at 24 h post dose.","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Jul","modification":"2026-06-01T07:37:35.54Z","creation":"2025-04-04T11:25:14.621Z"},"accession":"S-EPMC11228022","cross_references":{"pubmed":["38972904"],"doi":["10.1038/s41392-024-01895-0"]}}