{"type":"ScholarlyArticle","title":"Zero-shot Learning for Preclinical Drug Screening","authors":[{"name":"Kun Li","slug":"kun-li","given":"Kun","family":"Li","affiliation":"School of Computer Science , Wuhan University , Wuhan , China"},{"name":"Weiwei Liu","slug":"weiwei-liu","given":"Weiwei","family":"Liu","affiliation":"School of Computer Science , Wuhan University , Wuhan , China"},{"name":"Yong Luo","slug":"yong-luo","given":"Yong","family":"Luo","affiliation":"School of Computer Science , Wuhan University , Wuhan , China"},{"name":"Xiantao Cai","slug":"xiantao-cai","given":"Xiantao","family":"Cai","affiliation":"School of Computer Science , Wuhan University , Wuhan , China"},{"name":"Jia Wu","slug":"jia-wu","given":"Jia","family":"Wu","affiliation":"Department of Computing , Macquarie University , Sydney , Australia"},{"name":"Wenbin Hu","slug":"wenbin-hu","given":"Wenbin","family":"Hu","affiliation":"School of Computer Science , Wuhan University , Wuhan , China"}],"abstract":"Conventional deep learning methods typically employ supervised learning for drug response prediction (DRP). This entails dependence on labeled response data from drugs for model training. However, practical applications in the preclinical drug screening phase demand that DRP models predict responses for novel compounds, often with unknown drug responses. This presents a challenge, rendering supervised deep learning methods unsuitable for such scenarios.  In this paper, we propose a zero-shot learning solution for the DRP task in preclinical drug screening. Specifically, we propose a Multi-branch Multi-Source Domain Adaptation Test Enhancement Plug-in, called MSDA. MSDA can be seamlessly integrated with conventional DRP methods, learning invariant features from the prior response data of similar drugs to enhance real-time predictions of unlabeled compounds. The results of experiments on two large drug response datasets showed that MSDA efficiently predicts drug responses for novel compounds, leading to a general performance improvement of 5-10% in the preclinical drug screening phase.  The significance of this solution resides in its potential to accelerate the drug discovery process, improve drug candidate assessment, and facilitate the success of drug discovery. The code is available at https://github.com/DrugD/MSDA.","doi":"10.24963/ijcai.2024/234","url":"https://ijcai.org/proceedings/2024/234","pdf":"https://ijcai.org/proceedings/2024/0234.pdf","markdown":"https://ijcai.org/proceedings/2024/234.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/234","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":"Data Mining","track":"Main Track","number":234,"pages":{"first":2117,"last":2125},"keywords":["Multidisciplinary Topics and Applications: MTA: Bioinformatics","Data Mining: DM: Mining graphs","Data Mining: DM: Knowledge graphs and knowledge base completion"],"videos":[],"references":[{"position":1,"raw":"[Bai et al., 2023] Peizhen Bai, Filip Miljković, Bino John, and Haiping Lu. 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