A Novel Application of a Generation Model in Foreseeing 'Future' Reactions
文献类型：期刊论文
| 作者 | Cao, Lujing2; Wu, Yejian2; Zhuang, Yixin2; Xiong, Linan2; Zhan, Zhajun2; Ma, Liefeng2; Duan, Hongliang1,2 |
| 刊名 | SYNLETT
 |
| 出版日期 | 2022-10-07 |
| 页码 | 7 |
| 关键词 | deep learning artificial intelligence reaction generation Michael reaction synthesis design |
| ISSN号 | 0936-5214 |
| DOI | 10.1055/a-1937-9113 |
| 通讯作者 | Ma, Liefeng(maliefeng@zjut.edu.cn) ; Duan, Hongliang(hduan@zjut.edu.cn) |
| 英文摘要 | Deep learning is widely used in chemistry and can rival human chemists in certain scenarios. Inspired by molecule generation in new drug discovery, we present a deep-learning-based approach to reaction generation with the Trans-VAE model. To examine how exploratory and innovative the model is in reaction generation, we constructed the data set by time splitting. We used the Michael addition reaction as a generation vehicle and took these reactions reported before a certain date as the training set and explored whether the model could generate reactions that were reported after that date. We took 2010 and 2015 as time points for splitting the reported Michael addition reaction; among the generated reactions, 911 and 487 reactions were applied in the experiments after the respective split time points, accounting for 12.75% and 16.29% of all reported reactions after each time point. The generated results were in line with expectations and a large number of new, chemically feasible, Michael addition reactions were generated, which further demonstrated the ability of the Trans-VAE model to learn reaction rules. Our research provides a reference for the future discovery of novel reactions by using deep learning. |
| WOS关键词 | NEURAL-NETWORK ; PREDICTION |
| 资助项目 | National Natural Science Foundation of China[81903438] ; Natural Science Foundation of Zhejiang Province[LD22H300004] |
| WOS研究方向 | Chemistry |
| 语种 | 英语 |
| WOS记录号 | WOS:000864857500001 |
| 出版者 | GEORG THIEME VERLAG KG |
| 源URL | [http://119.78.100.183/handle/2S10ELR8/302748]  |
| 专题 | 新药研究国家重点实验室 |
| 通讯作者 | Ma, Liefeng; Duan, Hongliang |
| 作者单位 | 1.Chinese Acad Sci, Shanghai Inst Mat Med SIMM, State Key Lab Drug Res, Shanghai 201203, Peoples R China 2.Zhejiang Univ Technol, Coll Pharmaceut Sci, Hangzhou 310014, Peoples R China |
| 推荐引用方式 GB/T 7714 | Cao, Lujing,Wu, Yejian,Zhuang, Yixin,et al. A Novel Application of a Generation Model in Foreseeing 'Future' Reactions[J]. SYNLETT,2022:7. |
| APA | Cao, Lujing.,Wu, Yejian.,Zhuang, Yixin.,Xiong, Linan.,Zhan, Zhajun.,...&Duan, Hongliang.(2022).A Novel Application of a Generation Model in Foreseeing 'Future' Reactions.SYNLETT,7. |
| MLA | Cao, Lujing,et al."A Novel Application of a Generation Model in Foreseeing 'Future' Reactions".SYNLETT (2022):7. |
入库方式： OAI收割
来源：上海药物研究所
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