研究

葉祝一帆 Ye Zhuyifan

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 個人簡介

葉祝一帆是澳門理工大學人工智能藥物發現中心的一位講師。他的研究方向是應用人工智能來解決藥劑學和藥物發現方面的挑戰，包括晶體結構預測、應用先進前沿的機器學習算法、處理小型不平衡數據、藥代動力學參數預測、用生成和判別模型進行藥物反向設計、以及有機溶解度預測。在此之前，他在澳門大學獲得了生物醫藥科學的博士學位。

 學習工作經歷

學習經歷：
2018 - 2022: 博士，澳門大學
2016 - 2018: 碩士，澳門大學
2012 - 2016: 學士，中國藥科大學

工作經歷：
2023 - 至今：講師，澳門理工大學

 研究領域

• 人工智能應用於藥劑學中：晶體結構預測、應用先進前沿的機器學習算法、處理小型不平衡數據
• 人工智能應用於藥物發現中：藥代動力學參數預測、用生成和判別模型進行藥物反向設計、有機溶劑度預測

 研究成果

1. Run Han, Zhuyifan Ye, et al. Predicting liposome formulations by the integrated machine learning and molecular modeling approaches, Asian Journal of Pharmaceutical Sciences, 2023, 18(3), 100811. (Co-first author, JCR Q1, IF=10.2)
2. Nannan Wang, Yunsen Zhang, Wei Wang, Zhuyifan Ye, et al. How can machine learning and multiscale modeling benefit ocular drug development?, Advanced Drug Delivery Reviews, 2023, 196, 114772. (JCR Q1, IF=16.1)
3. Jiayin Deng, Zhuyifan Ye, et al. Machine learning in accelerating microsphere formulation development, Drug Delivery and Translational Research, 2023, 13(4), pp. 966-982. (Co-first author, JCR Q1, IF=5.4)
4. Wenwen Zheng, Junjun Li, Yu Wang, Zhuyifan Ye, et al. Quantitative Analysis for Chinese and US-listed Pharmaceutical Companies by the LightGBM Algorithm, Current computer-aided drug design, 2023, 13(4), pp. 966-982. (JCR Q4, IF=1.7)
5. Haoshi Gao, Stanislav Kan, Zhuyifan Ye, et al. Development of in silico methodology for siRNA lipid nanoparticle formulations, Chemical Engineering Journal, 2022, 442, 136310. (Co-first author, JCR Q1, IF=15.1)
6. Wei Wang, Shuo Feng, Zhuyifan Ye, et al. Prediction of lipid nanoparticles for mRNA vaccines by the machine learning algorithm, Acta Pharmaceutica Sinica B, 2022, 12(6), pp. 2950-2962. (Co-first author, JCR Q1, IF=14.5)
7. Junjun Li, Hanlu Gao, Zhuyifan Ye, et al. In silico formulation prediction of drug/cyclodextrin/polymer ternary complexes by machine learning and molecular modeling techniques, Carbohydrate Polymers, 2022, 275, 118712. (JCR Q1, IF=11.2)
8. Zhuyifan Ye, Defang Ouyang. Prediction of small-molecule compound solubility in organic solvents by machine learning algorithms, Journal of Cheminformatics, 2021, 13(1), 98. (JCR Q1, IF=8.6)
9. Zhuyifan Ye, Wenmian Yang, et al. Interpretable machine learning methods for in vitro pharmaceutical formulation development, Food Frontiers, 2021, 2, pp. 195-207. (JCR Q1, IF=9.9)
10. Wei Wang, Zhuyifan Ye, et al. Computational pharmaceutics-A new paradigm of drug delivery, Journal of Controlled Release, 2021, 338, pp. 119-136. (Co-first author, JCR Q1, IF=10.8)
11. Hanlu Gao, Wei Wang, Jie Dong, Zhuyifan Ye, et al. An integrated computational methodology with data-driven machine learning, molecular modeling and PBPK modeling to accelerate solid dispersion formulation design, European Journal of Pharmaceutics and Biopharmaceutics, 2021, 158, pp. 336-346. (JCR Q1, IF=4.9)
12. Yuan He, Zhuyifan Ye, et al. Can machine learning predict drug nanocrystals?, Journal of Controlled Release, 2020, 322, pp. 274–285. (Co-first author, JCR Q1, IF=10.8, Cover)
13. Haoshi Gao, Zhuyifan Ye, et al. Predicting drug/phospholipid complexation by the lightGBM method, Chemical Physics Letters, 2020, 747, 137354. (JCR Q3, IF=2.8)
14. Qianqian Zhao, Zhuyifan Ye, et al. Predicting complexation performance between cyclodextrins and guest molecules by integrated machine learning and molecular modeling techniques, Acta Pharmaceutica Sinica B, 2019, 9(6), pp. 1241-1252. (JCR Q1, IF=14.5)
15. Run Han, Hui Xiong, Zhuyifan Ye, et al. Predicting physical stability of solid dispersions by machine learning techniques, Journal of Controlled Release, 2019, 311-312, pp. 16-25. (Co-first author, JCR Q1, IF=10.8, Cover)
16. Zhuyifan Ye, Yilong Yang, et al. An integrated transfer learning and multitask learning approach for pharmacokinetic parameter prediction, Molecular Pharmaceutics, 2019, 16(2), pp. 533-541. (JCR Q1, IF=4.9)
17. Yilong Yang, Zhuyifan Ye, et al. Deep learning for in vitro prediction of pharmaceutical formulations, Acta Pharmaceutica Sinica B, 2019, 9(1), pp. 177-185. (Co-first author, JCR Q1, IF=14.5)