{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Moon K"],"funding":["National Research Foundation of Korea (NRF) grant funded by the Korea government"],"pagination":["btad371"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11249084"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["39(6)"],"pubmed_abstract":["<h4>Motivation</h4>Self-supervised learning (SSL) is a method that learns the data representation by utilizing supervision inherent in the data. This learning method is in the spotlight in the drug field, lacking annotated data due to time-consuming and expensive experiments. SSL using enormous unlabeled data has shown excellent performance for molecular property prediction, but a few issues exist. (i) Existing SSL models are large-scale; there is a limitation to implementing SSL where the computing resource is insufficient. (ii) In most cases, they do not utilize 3D structural information for molecular representation learning. The activity of a drug is closely related to the structure of the drug molecule. Nevertheless, most current models do not use 3D information or use it partially. (i"],"journal":["Bioinformatics (Oxford, England)"],"pubmed_title":["3D graph contrastive learning for molecular property prediction."],"pmcid":["PMC11249084"],"funding_grant_id":["2022R1C1C1005065"],"pubmed_authors":["Kwon S","Im HJ","Moon K"],"additional_accession":[]},"is_claimable":false,"name":"3D graph contrastive learning for molecular property prediction.","description":"<h4>Motivation</h4>Self-supervised learning (SSL) is a method that learns the data representation by utilizing supervision inherent in the data. This learning method is in the spotlight in the drug field, lacking annotated data due to time-consuming and expensive experiments. SSL using enormous unlabeled data has shown excellent performance for molecular property prediction, but a few issues exist. (i) Existing SSL models are large-scale; there is a limitation to implementing SSL where the computing resource is insufficient. (ii) In most cases, they do not utilize 3D structural information for molecular representation learning. The activity of a drug is closely related to the structure of the drug molecule. Nevertheless, most current models do not use 3D information or use it partially. (i","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Jan","modification":"2025-04-19T19:40:38.055Z","creation":"2025-04-19T19:40:38.055Z"},"accession":"S-EPMC11249084","cross_references":{"pubmed":["37289553"],"doi":["10.1093/bioinformatics/btad371"]}}