Deep learning in retrosynthesis planning: datasets, models and tools. Issue 1 (24th September 2021)

• Record Type: Journal Article
• Authors: Dong, Jingxin; Zhao, Mingyi; Liu, Yuansheng; Su, Yansen; Zeng, Xiangxiang
• Is Part Of: Briefings in bioinformatics. Volume 23:Issue 1(2022)
• Publication Date: 2021-09-24
• Subjects: deep learning -- graph neural network -- retrosynthesis -- seq2seq -- transformer; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bbab391
• Languages: English
• ISSNs: 1467-5463

Abstract

Abstract: In recent years, synthesizing drugs powered by artificial intelligence has brought great convenience to society. Since retrosynthetic analysis occupies an essential position in synthetic chemistry, it has received broad attention from researchers. In this review, we comprehensively summarize the development process of retrosynthesis in the context of deep learning. This review covers all aspects of retrosynthesis, including datasets, models and tools. Specifically, we report representative models from academia, in addition to a detailed description of the available and stable platforms in the industry. We also discuss the disadvantages of the existing models and provide potential future trends, so that more abecedarians will quickly understand and participate in the family of retrosynthesis planning.