{"type":"ScholarlyArticle","title":"A Unified View of Deep Learning for Reaction and Retrosynthesis Prediction: Current Status and Future Challenges","authors":[{"name":"Ziqiao Meng","slug":"ziqiao-meng","given":"Ziqiao","family":"Meng","affiliation":"The Chinese University of Hong Kong"},{"name":"Peilin Zhao","slug":"peilin-zhao","given":"Peilin","family":"Zhao","affiliation":"Tencent AI Lab"},{"name":"Yang Yu","slug":"yang-yu","given":"Yang","family":"Yu","affiliation":"Tencent AI Lab"},{"name":"Irwin King","slug":"irwin-king","given":"Irwin","family":"King","affiliation":"The Chinese University of Hong Kong"}],"abstract":"Reaction and retrosynthesis prediction are two fundamental tasks in computational chemistry. In recent years, these two tasks have attracted great attentions from both machine learning and drug discovery communities. Various deep learning approaches have been proposed to tackle these two problems and achieved initial success. In this survey, we conduct a comprehensive investigation on advanced deep learning-based reaction and retrosynthesis prediction models. We first summarize the design mechanism, strengths and weaknesses of the state-of-the-art approaches. Then we further discuss limitations of current solutions and open challenges in the problem itself. Last but not the least, we present some promising directions to facilitate future research. To our best knowledge, this paper is the first comprehensive and systematic survey on unified understanding of reaction and retrosynthesis prediction.","doi":"10.24963/ijcai.2023/753","url":"https://ijcai.org/proceedings/2023/753","pdf":"https://ijcai.org/proceedings/2023/0753.pdf","markdown":"https://ijcai.org/proceedings/2023/753.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/753","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":753,"pages":{"first":6723,"last":6731},"keywords":["Survey: Multidisciplinary Topics and Applications"],"videos":[],"references":[{"position":1,"raw":"[Bi et al., 2021] Hangrui Bi, Hengyi Wang, Chence Shi, Connor W. 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