Literature DB >> 34571535

Deep learning in retrosynthesis planning: datasets, models and tools.

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.
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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


  4 in total

Review 1.  Machine learning: its challenges and opportunities in plant system biology.

Authors:  Mohsen Hesami; Milad Alizadeh; Andrew Maxwell Phineas Jones; Davoud Torkamaneh
Journal:  Appl Microbiol Biotechnol       Date:  2022-05-16       Impact factor: 4.813

2.  Immunoglobulin Classification Based on FC* and GC* Features.

Authors:  Hao Wan; Jina Zhang; Yijie Ding; Hetian Wang; Geng Tian
Journal:  Front Genet       Date:  2022-01-24       Impact factor: 4.599

Review 3.  Research on the Computational Prediction of Essential Genes.

Authors:  Yuxin Guo; Ying Ju; Dong Chen; Lihong Wang
Journal:  Front Cell Dev Biol       Date:  2021-12-06

Review 4.  AOPM: Application of Antioxidant Protein Classification Model in Predicting the Composition of Antioxidant Drugs.

Authors:  Yixiao Zhai; Jingyu Zhang; Tianjiao Zhang; Yue Gong; Zixiao Zhang; Dandan Zhang; Yuming Zhao
Journal:  Front Pharmacol       Date:  2022-01-18       Impact factor: 5.810

  4 in total

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