{"type":"ScholarlyArticle","title":"ZeroDDI: A Zero-Shot Drug-Drug Interaction Event Prediction Method with Semantic Enhanced Learning and Dual-modal Uniform Alignment","authors":[{"name":"Ziyan Wang","slug":"ziyan-wang","given":"Ziyan","family":"Wang","affiliation":"College of Informatics , Huazhong Agricultural University , Wuhan 430070 , China"},{"name":"Zhankun Xiong","slug":"zhankun-xiong","given":"Zhankun","family":"Xiong","affiliation":"College of Informatics , Huazhong Agricultural University , Wuhan 430070 , China"},{"name":"Feng Huang","slug":"feng-huang","given":"Feng","family":"Huang","affiliation":"College of Informatics , Huazhong Agricultural University , Wuhan 430070 , China"},{"name":"Xuan Liu","slug":"xuan-liu","given":"Xuan","family":"Liu","affiliation":"College of Informatics , Huazhong Agricultural University , Wuhan 430070 , China"},{"name":"Wen Zhang","slug":"wen-zhang","given":"Wen","family":"Zhang","affiliation":"College of Informatics , Huazhong Agricultural University , Wuhan 430070 , China"}],"abstract":"Drug-drug interactions (DDIs) can result in various pharmacological changes, which can be categorized into different classes known as DDI events (DDIEs). In recent years, previously unobserved/unseen DDIEs have been emerging, posing a new classification task when unseen classes have no labelled instances in the training stage, which is formulated as a zero-shot DDIE prediction (ZS-DDIE) task. However, existing computational methods are not directly applicable to ZS-DDIE, which has two primary challenges: obtaining suitable DDIE representations and handling the class imbalance issue. To overcome these challenges, we propose a novel method named ZeroDDI for the ZS-DDIE task. Specifically, we design a biological semantic enhanced DDIE representation learning module, which emphasizes the key biological semantics and distills discriminative molecular substructure-related semantics for DDIE representation learning. Furthermore, we propose a dual-modal uniform alignment strategy to distribute drug pair representations and DDIE semantic representations uniformly in unit sphere and align the matched ones, which can mitigate the issue of class imbalance. Extensive experiments showed that ZeroDDI surpasses the baselines and indicate that it is a promising tool for detecting unseen DDIEs. Our code has been released in https://github.com/wzy-Sarah/ZeroDDI.","doi":"10.24963/ijcai.2024/671","url":"https://ijcai.org/proceedings/2024/671","pdf":"https://ijcai.org/proceedings/2024/0671.pdf","markdown":"https://ijcai.org/proceedings/2024/671.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/671","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":671,"pages":{"first":6071,"last":6079},"keywords":["Multidisciplinary Topics and Applications: MTA: Health and medicine","Multidisciplinary Topics and Applications: MTA: Bioinformatics"],"videos":[],"references":[{"position":1,"raw":"[Akata et al., 2016] Zeynep Akata, Florent Perronnin, Zaid Harchaoui, and Cordelia Schmid. 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