{"type":"ScholarlyArticle","title":"An Image-enhanced Molecular Graph Representation Learning Framework","authors":[{"name":"Hongxin Xiang","slug":"hongxin-xiang","given":"Hongxin","family":"Xiang","affiliation":"College of Computer Science and Electronic Engineering , Hunan University , Changsha , China; Department of AIDD , Shanghai Yuyao Biotechnology Co., Ltd. , Shanghai , China"},{"name":"Shuting Jin","slug":"shuting-jin","given":"Shuting","family":"Jin","affiliation":"School of Computer Science & Technology , Wuhan University of Science and Technology , Wuhan , China"},{"name":"Jun Xia","slug":"jun-xia","given":"Jun","family":"Xia","affiliation":"School of Engineering , Westlake University , Hangzhou , China"},{"name":"Man Zhou","slug":"man-zhou","given":"Man","family":"Zhou","affiliation":"University of Science and Technology of China , Hefei , China"},{"name":"Jianmin Wang","slug":"jianmin-wang","given":"Jianmin","family":"Wang","affiliation":"The Interdisciplinary Graduate Program in Integrative Biotechnology , Yonsei University , Incheon , Korea"},{"name":"Li Zeng","slug":"li-zeng","given":"Li","family":"Zeng","affiliation":"Department of AIDD , Shanghai Yuyao Biotechnology Co., Ltd. , Shanghai , China"},{"name":"Xiangxiang Zeng","slug":"xiangxiang-zeng","given":"Xiangxiang","family":"Zeng","affiliation":"College of Computer Science and Electronic Engineering , Hunan University , Changsha , China"}],"abstract":"Extracting rich molecular representation is a crucial prerequisite for accurate drug discovery. Recent molecular representation learning methods achieve impressive progress, but the paradigm of learning from a single modality gradually encounters the bottleneck of limited representation capabilities. In this work, we fully consider the rich visual information contained in 3D conformation molecular images (i.e., texture, shadow, color and planar spatial information) and distill graph-based models for more discriminative drug discovery. Specifically, we propose an image-enhanced molecular graph representation learning framework that leverages multi-view molecular images rendered from 3D conformations to boost molecular graph representations. To extract useful auxiliary knowledge from multi-view images, we design a teacher, which is pre-trained on 2 million molecules with conformations through five meticulously designed pre-training tasks. To transfer knowledge from teacher to graph-based students, we pose an efficient cross-modal knowledge distillation strategy with knowledge enhancer and task enhancer. It is worth noting that the distillation architecture of IEM can be directly integrated into existing graph-based models, and significantly improves the capabilities of these models (e.g. GIN, EdgePred, GraphMVP, MoleBERT) for molecular representation learning. In particular, GraphMVP and MoleBERT equipped with IEM achieve new state-of-the-art performance on MoleculeNet benchmark, achieving average 73.89% and 73.81% ROC-AUC, respectively. Code is available at https://github.com/HongxinXiang/IEM.","doi":"10.24963/ijcai.2024/675","url":"https://ijcai.org/proceedings/2024/675","pdf":"https://ijcai.org/proceedings/2024/0675.pdf","markdown":"https://ijcai.org/proceedings/2024/675.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/675","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Multidisciplinary Topics and Applications","track":"Main Track","number":675,"pages":{"first":6107,"last":6115},"keywords":["Machine Learning: ML: Representation learning","Multidisciplinary Topics and Applications: MTA: Bioinformatics","Machine Learning: ML: Knowledge-aided learning","Machine Learning: ML: Self-supervised Learning"],"videos":[],"references":[{"position":1,"raw":"[Arnab et al., 2021] Anurag Arnab, Mostafa Dehghani, Georg Heigold, Chen Sun, Mario Lučić, and Cordelia Schmid. 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