{"type":"ScholarlyArticle","title":"Graph Sampling-based Meta-Learning for Molecular Property Prediction","authors":[{"name":"Xiang Zhuang","slug":"xiang-zhuang","given":"Xiang","family":"Zhuang","affiliation":"College of Computer Science and Technology , Zhejiang University; ZJU-Hangzhou Global Scientific and Technological Innovation Center; Alibaba-Zhejiang University Joint Research Institute of Frontier Technologies"},{"name":"Qiang Zhang","slug":"qiang-zhang","given":"Qiang","family":"Zhang","affiliation":"College of Computer Science and Technology , Zhejiang University; ZJU-Hangzhou Global Scientific and Technological Innovation Center"},{"name":"Bin Wu","slug":"bin-wu","given":"Bin","family":"Wu","affiliation":""},{"name":"Keyan Ding","slug":"keyan-ding","given":"Keyan","family":"Ding","affiliation":"ZJU-Hangzhou Global Scientific and Technological Innovation Center"},{"name":"Yin Fang","slug":"yin-fang","given":"Yin","family":"Fang","affiliation":"College of Computer Science and Technology , Zhejiang University; ZJU-Hangzhou Global Scientific and Technological Innovation Center; Alibaba-Zhejiang University Joint Research Institute of Frontier Technologies"},{"name":"Huajun Chen","slug":"huajun-chen","given":"Huajun","family":"Chen","affiliation":"College of Computer Science and Technology , Zhejiang University; ZJU-Hangzhou Global Scientific and Technological Innovation Center; Alibaba-Zhejiang University Joint Research Institute of Frontier Technologies"}],"abstract":"Molecular property is usually observed with a limited number of samples, and researchers have considered property prediction as a few-shot problem. One important fact that has been ignored by prior works is that each molecule can be recorded with several different properties simultaneously. To effectively utilize many-to-many correlations of molecules and properties, we propose a Graph Sampling-based Meta-learning (GS-Meta) framework for few-shot molecular property prediction. First, we construct a Molecule-Property relation Graph (MPG): molecule and properties are nodes, while property labels decide edges. Then, to utilize the topological information of MPG,  we reformulate an episode in meta-learning as a subgraph of the MPG, containing a target property node, molecule nodes, and auxiliary property nodes. Third, as episodes in the form of subgraphs are no longer independent of each other, we propose to schedule the subgraph sampling process with a contrastive loss function, which considers the consistency and discrimination of subgraphs. Extensive experiments on 5 commonly-used benchmarks show GS-Meta consistently outperforms state-of-the-art methods by  5.71%-6.93% in ROC-AUC and verify the effectiveness of each proposed module. Our code is available at https://github.com/HICAI-ZJU/GS-Meta.","doi":"10.24963/ijcai.2023/526","url":"https://ijcai.org/proceedings/2023/526","pdf":"https://ijcai.org/proceedings/2023/0526.pdf","markdown":"https://ijcai.org/proceedings/2023/526.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/526","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":"Machine Learning","track":"Main Track","number":526,"pages":{"first":4729,"last":4737},"keywords":["Multidisciplinary Topics and Applications: MDA: Bioinformatics","Machine Learning: ML: Meta-learning","Machine Learning: ML: Few-shot learning"],"videos":[],"references":[{"position":1,"raw":"[Altae-Tran et al., 2017] Han Altae-Tran, Bharath Ramsun- dar, Aneesh S Pappu, and Vijay Pande. 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