biostudies-literatureMoon KNational Research Foundation of Korea (NRF) grant funded by the Korea governmentbtad371https://www.ebi.ac.uk/biostudies/studies/S-EPMC11249084biostudies-literatureUnknown39(6)<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. (iBioinformatics (Oxford, England)3D graph contrastive learning for molecular property prediction.PMC112490842022R1C1C1005065Kwon SIm HJMoon Kfalse3D graph contrastive learning for molecular property prediction.<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. (i2022-01-01T00:00:00Z2022 Jan2025-04-19T19:40:38.055Z2025-04-19T19:40:38.055ZS-EPMC112490843728955310.1093/bioinformatics/btad371