AI-Powered Mining of Highly Customized and Superior ESIPT-Based Fluorescent Probes
文献类型：期刊论文
| 作者 | Huang, Wenzhi2; Huang, Shuai2; Fang, Yanpeng2; Zhu, Tianyu2; Chu, Feiyi2; Liu, Qianhui2; Yu, Kunqian1 ; Chen, Fei2; Dong, Jie2; Zeng, Wenbin2
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| 刊名 | ADVANCED SCIENCE
 |
| 出版日期 | 2024-07-17 |
| 页码 | 12 |
| 关键词 | artificial intelligence ESIPT fluorescent probe machine learning virtual screening |
| DOI | 10.1002/advs.202405596 |
| 通讯作者 | Dong, Jie(jiedong@csu.edu.cn) ; Zeng, Wenbin(wbzeng@csu.edu.cn) |
| 英文摘要 | Excited-state intramolecular proton transfer (ESIPT) has attracted great attention in fluorescent sensors and luminescent materials due to its unique photobiological and photochemical features. However, the current structures are far from meeting the specific demands for ESIPT molecules in different scenarios; the try-and-error development method is labor-intensive and costly. Therefore, it is imperative to devise novel approaches for the exploration of promising ESIPT fluorophores. This research proposes an artificial intelligence approach aiming at exploring ESIPT molecules efficiently. The first high-quality ESIPT dataset and a multi-level prediction system are constructed that realized accurate identification of ESIPT molecules from a large number of compounds under a stepwise distinguishing from conventional molecules to fluorescent molecules and then to ESIPT molecules. Furthermore, key structural features that contributed to ESIPT are revealed by using the SHapley Additive exPlanations (SHAP) method. Then three strategies are proposed to ensure the ESIPT process while keeping good safety, pharmacokinetic properties, and novel structures. With these strategies, >700 previously unreported ESIPT molecules are screened from a large pool of 570 000 compounds. The ESIPT process and biosafety of optimal molecules are successfully validated by quantitative calculation and experiment. This novel approach is expected to bring a new paradigm for exploring ideal ESIPT molecules. |
| WOS关键词 | INTRAMOLECULAR PROTON-TRANSFER ; PLATFORM ; DATABASE |
| 资助项目 | National Natural Science Foundation of China[22003078] ; National Natural Science Foundation of China[82272067] ; National Natural Science Foundation of China[M-0696] ; Science and Technology Program of Hunan Province[2022JJ80052] ; Innovation-Driven Project of Central South University[2023CXQD004] |
| WOS研究方向 | Chemistry ; Science & Technology - Other Topics ; Materials Science |
| 语种 | 英语 |
| WOS记录号 | WOS:001271652700001 |
| 出版者 | WILEY |
| 源URL | [http://119.78.100.183/handle/2S10ELR8/312395]  |
| 专题 | 新药研究国家重点实验室 |
| 通讯作者 | Dong, Jie; Zeng, Wenbin |
| 作者单位 | 1.Chinese Acad Sci, Shanghai Inst Mat Med, Drug Discovery & Design Ctr, State Key Lab Drug Res, Shanghai 201203, Peoples R China 2.Cent South Univ, Xiangya Sch Pharmaceut Sci, Changsha 410083, Peoples R China |
| 推荐引用方式 GB/T 7714 | Huang, Wenzhi,Huang, Shuai,Fang, Yanpeng,et al. AI-Powered Mining of Highly Customized and Superior ESIPT-Based Fluorescent Probes[J]. ADVANCED SCIENCE,2024:12. |
| APA | Huang, Wenzhi.,Huang, Shuai.,Fang, Yanpeng.,Zhu, Tianyu.,Chu, Feiyi.,...&Zeng, Wenbin.(2024).AI-Powered Mining of Highly Customized and Superior ESIPT-Based Fluorescent Probes.ADVANCED SCIENCE,12. |
| MLA | Huang, Wenzhi,et al."AI-Powered Mining of Highly Customized and Superior ESIPT-Based Fluorescent Probes".ADVANCED SCIENCE (2024):12. |
入库方式： OAI收割
来源：上海药物研究所
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