Research

葉祝一帆 Ye Zhuyifan

---

 Biography

Zhuyifan Ye is a Lecturer at the Centre for Artificial Intelligence Driven Drug Discovery at Macao Polytechnic University (MPU). His research interests are in applying Artificial Intelligence to tackle challenges in Pharmaceutics and Drug Discovery, including Crystal Structure Prediction, Application of Advanced Machine Learning Algorithms, Handling Small Imbalanced Data, Pharmacokinetic Parameter Prediction, Drug Inverse Design with Generative and Discriminative Models, and Organic Solubility Prediction. Prior to that, he received his Ph.D. in Biomedical Sciences from University of Macau.

 Education and Experiences

Educational background
2018 - 2022: Ph.D., University of Macau
2016 - 2018: M.S., University of Macau
2012 - 2016: B.S., China Pharmaceutical University

Work experiences:
2023 - present: Lecturer, Macao Polytechnic University

 Research Interests

• Artificial Intelligence in Pharmaceutics: Crystal Structure Prediction, Application of Advanced Machine Learning Algorithms, Handling Small Imbalanced Data
• Artificial Intelligence in Drug Discovery: Pharmacokinetic Parameter Prediction, Drug Inverse Design with Generative and Discriminative Models, Organic Solubility Prediction

 Selected Publications

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)