| Literature DB >> 34571535 |
Jingxin Dong1, Mingyi Zhao2, Yuansheng Liu1, Yansen Su3, Xiangxiang Zeng1.
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.Entities:
Keywords: deep learning; graph neural network; retrosynthesis; seq2seq; transformer
Mesh:
Year: 2022 PMID: 34571535 DOI: 10.1093/bib/bbab391
Source DB: PubMed Journal: Brief Bioinform ISSN: 1467-5463 Impact factor: 11.622