{"type":"ScholarlyArticle","title":"Graph-based Molecular Representation Learning","authors":[{"name":"Zhichun Guo","slug":"zhichun-guo","given":"Zhichun","family":"Guo","affiliation":"University of Notre Dame"},{"name":"Kehan Guo","slug":"kehan-guo","given":"Kehan","family":"Guo","affiliation":"University of Notre Dame"},{"name":"Bozhao Nan","slug":"bozhao-nan","given":"Bozhao","family":"Nan","affiliation":"University of Notre Dame"},{"name":"Yijun Tian","slug":"yijun-tian","given":"Yijun","family":"Tian","affiliation":"University of Notre Dame"},{"name":"Roshni G. Iyer","slug":"roshni-g-iyer","given":"Roshni G.","family":"Iyer","affiliation":"University of California , Los Angeles"},{"name":"Yihong Ma","slug":"yihong-ma","given":"Yihong","family":"Ma","affiliation":"University of Notre Dame"},{"name":"Olaf Wiest","slug":"olaf-wiest","given":"Olaf","family":"Wiest","affiliation":"University of Notre Dame"},{"name":"Xiangliang Zhang","slug":"xiangliang-zhang","given":"Xiangliang","family":"Zhang","affiliation":"University of Notre Dame"},{"name":"Wei Wang","slug":"wei-wang","given":"Wei","family":"Wang","affiliation":"University of California , Los Angeles"},{"name":"Chuxu Zhang","slug":"chuxu-zhang","given":"Chuxu","family":"Zhang","affiliation":"Brandeis University"},{"name":"Nitesh V. Chawla","slug":"nitesh-v-chawla","given":"Nitesh V.","family":"Chawla","affiliation":"University of Notre Dame"}],"abstract":"Molecular representation learning (MRL) is a key step to build the connection between machine learning and chemical science. In particular, it encodes molecules as numerical vectors preserving the molecular structures and features, on top of which the downstream tasks (e.g., property prediction) can be performed. Recently, MRL has achieved considerable progress, especially in methods based on deep molecular graph learning. In this survey, we systematically review these graph-based molecular representation techniques, especially the methods incorporating chemical domain knowledge. Specifically, we first introduce the features of 2D and 3D molecular graphs. Then we summarize and categorize MRL methods into three groups based on their input. Furthermore, we discuss some typical chemical applications supported by MRL. To facilitate studies in this fast-developing area, we also list the benchmarks and commonly used datasets in the paper. Finally, we share our thoughts on future research directions.","doi":"10.24963/ijcai.2023/744","url":"https://ijcai.org/proceedings/2023/744","pdf":"https://ijcai.org/proceedings/2023/0744.pdf","markdown":"https://ijcai.org/proceedings/2023/744.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/744","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Survey Track","track":"Survey Track","number":744,"pages":{"first":6638,"last":6646},"keywords":["Survey: Machine Learning","Survey: Data Mining"],"videos":[],"references":[{"position":1,"raw":"[Ahneman et al., 2018] Derek T Ahneman, Jesús G Estrada, Shishi Lin, Spencer D Dreher, and Abigail G Doyle. 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