From theory to experiment: transformer-based generation enables rapid discovery of novel reactions
文献类型：期刊论文
| 作者 | Wang, Xinqiao5; Yao, Chuansheng2,3; Zhang, Yun5; Yu, Jiahui5; Qiao, Haoran1; Zhang, Chengyun5; Wu, Yejian5; Bai, Renren2,3; Duan, Hongliang4,5 |
| 刊名 | JOURNAL OF CHEMINFORMATICS
 |
| 出版日期 | 2022-09-02 |
| 卷号 | 14期号:1页码:14 |
| 关键词 | Deep learning Heck reactions Reaction generation |
| ISSN号 | 1758-2946 |
| DOI | 10.1186/s13321-022-00638-z |
| 通讯作者 | Bai, Renren(renrenbai@hznu.edu.cn) ; Duan, Hongliang(hduan@zjut.edu.cn) |
| 英文摘要 | Deep learning methods, such as reaction prediction and retrosynthesis analysis, have demonstrated their significance in the chemical field. However, the de novo generation of novel reactions using artificial intelligence technology requires further exploration. Inspired by molecular generation, we proposed a novel task of reaction generation. Herein, Heck reactions were applied to train the transformer model, a state-of-art natural language process model, to generate 4717 reactions after sampling and processing. Then, 2253 novel Heck reactions were confirmed by organizing chemists to judge the generated reactions. More importantly, further organic synthesis experiments were performed to verify the accuracy and feasibility of representative reactions. The total process, from Heck reaction generation to experimental verification, required only 15 days, demonstrating that our model has well-learned reaction rules in-depth and can contribute to novel reaction discovery and chemical space exploration. |
| WOS关键词 | ARYLATION ; DESIGN ; MODEL |
| 资助项目 | National Natural Science Foundation of China, NSFC[81903438] ; Natural Science Foundation of Zhejiang Province[LD22H300004] |
| WOS研究方向 | Chemistry ; Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000849207400001 |
| 出版者 | BMC |
| 源URL | [http://119.78.100.183/handle/2S10ELR8/302135]  |
| 专题 | 新药研究国家重点实验室 |
| 通讯作者 | Bai, Renren; Duan, Hongliang |
| 作者单位 | 1.Shanghai Univ Elect Power, Coll Math & Phys, Shanghai 201203, Peoples R China 2.Hangzhou Normal Univ, Collaborat Innovat Ctr Tradit Chinese Med Zhejian, Key Lab Elemene Class Anticanc Chinese Med, Engn Lab Dev & Applicat Tradit Chinese Med, Hangzhou, Peoples R China 3.Hangzhou Normal Univ, Coll Pharm, Sch Med, Hangzhou, Peoples R China 4.Chinese Acad Sci, Shanghai Inst Mat Med SIMM, State Key Lab Drug Res, Shanghai 201203, Peoples R China 5.Zhejiang Univ Technol, Coll Pharmaceut Sci, Artificial Intelligence Aided Drug Discovery Inst, Hangzhou 310014, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wang, Xinqiao,Yao, Chuansheng,Zhang, Yun,et al. From theory to experiment: transformer-based generation enables rapid discovery of novel reactions[J]. JOURNAL OF CHEMINFORMATICS,2022,14(1):14. |
| APA | Wang, Xinqiao.,Yao, Chuansheng.,Zhang, Yun.,Yu, Jiahui.,Qiao, Haoran.,...&Duan, Hongliang.(2022).From theory to experiment: transformer-based generation enables rapid discovery of novel reactions.JOURNAL OF CHEMINFORMATICS,14(1),14. |
| MLA | Wang, Xinqiao,et al."From theory to experiment: transformer-based generation enables rapid discovery of novel reactions".JOURNAL OF CHEMINFORMATICS 14.1(2022):14. |
入库方式： OAI收割
来源：上海药物研究所
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