Zhuyifan Ye, Ph.D., is a lecturer (PI) at Macao Polytechnic University, specializing in the application of machine learning and quantum mechanics to address challenges in biomedicine. I earned my Bachelor's degree from China Pharmaceutical University in 2016, and my Master's and Ph.D. degrees from University of Macau in 2018 and 2022, respectively. Since 2023, I have been a part of Macao Polytechnic University. 

My team focuses on developing artificial intelligence (AI) and machine learning methods to model the interactions between drugs and the body. We create machine learning methods for organic solid-state and continuous-phase systems, as well as the body. Additionally, we incorporate first-principles quantum mechanical methods to enhance the accuracy of our AI and machine learning models, enabling precise quantitative predictions in biomedicine. 

To date, I published 24 papers in SCI journals, with 21 appearing in JCR Q1 journals. I am the corresponding author, first author, or co-first author on 14 of these papers, one of which received the "Sixth Chinese Association for Science and Technology Outstanding Scientific Paper" award. My h-index is 17.

Highlights

• We are currently accepting applications from prospective Ph.D. students for the year 2027. We are offering scholarships, covering tuition fees and providing a living allowance for successful Ph.D. applicants.

• We are seeking self-motivated Ph.D. students to collaborate on exciting projects related to Quantum Machine Learning in biomedicine. Join our team and contribute to cutting-edge research in this field.

• For students with a strong passion for research and an interest in pursuing a Ph.D., we also welcome applications for Research Assistants (RA) and Internships (Remote).

• If you are enthusiastic about working with us, please reach out to us by sending an email to zhuyifanye[at]mpu.edu.mo.

• When contacting us, please attach your CV, including details such as your publications, ranking/GPA, English score, and any other relevant information.

• MPU students interested in conducting research in our laboratory are encouraged to connect with us. We welcome your participation.

Research Interests

Organic crystal structure prediction, Pharmaceutical formulation prediction, Pharmacokinetic parameter prediction, Organic solubility prediction, Interpretable machine learning approaches for biomedical data, and the application of first-principles quantum mechanical methods in machine learning modeling.

Prospective Students

We are committed to working closely with students, fostering a collaborative environment where we can tackle intriguing research topics together. We encourage open discussions to address challenges and embark on the journey of unraveling new knowledge. We will provide guidance, support, and valuable insights to help students grow and gain invaluable experience during their research journey.

Join our team and embark on an exciting research endeavor that will contribute to advancements in quantum mechanical and machine learning methods in biomedicine.

Publications

1. Tianqi Ma, Yating Qu, Chenxian Guan, Hang Yin, Wenmian Yang, Shing Fung Chow, Henry Hoi Yee Tong, Defang Ouyang*, Zhuyifan Ye*. Advances and Challenges in Machine Learning-based Image Analysis for Monitoring and Predicting Organic Crystal Formation, Aggregate, 2026. (JCR Q1, IF=14.5)

2. Jiayin Deng, Qiong Huang, Jiayi Lv, Yiqi Yang, Zhuyifan Ye, Yanyi Chu, Yiyang Wu, Qi Zhao*, Wei-Jie Fang*, Defang Ouyang*. Multimodal framework for early developability assessment to accelerate protein and antibody development, International Journal of Pharmaceutics, 2026. (JCR Q1, IF=6.0)

3. Bingwei Ni, Wanxiang Shen, Zhuyifan Ye*. TradePool: A Novel Interpretable Framework for Quantifying Atomic Attribution Values in Molecular Property Prediction, Journal of Chemical Information and Modeling, 2026. (JCR Q1, IF=6.4)

4. Wei Wang, Nannan Wang, Yiyang Wu, Zhuyifan Ye, Liang Zhao, Xianfeng Chen, Defang Ouyang*. An Integrated AI-PBPK Platform for Predicting Drug In Vivo Fate and Tissue Distribution in Human and Inter-Species Extrapolation, Clinical Pharmacology and Therapeutics, 2025. (JCR Q1, IF=5.5)

5. Shiwei Deng, Yiyang Wu, Zhuyifan Ye, Defang Ouyang*. In silico prediction of metabolic stability for ester-containing molecules: Machine learning and quantum mechanical methods, Chemometrics and Intelligent Laboratory Systems, 2025. (JCR Q2, IF=3.7)

6. Zheng Wu, Nannan Wang, Zhuyifan Ye, Huanle Xu, Ging Chan, Ouyang, Defang*. FormulationBCS: A Machine Learning Platform Based on Diverse Molecular Representations for Biopharmaceutical Classification System (BCS) Class Prediction, Molecular Pharmaceutics, 2024. (JCR Q1, IF=4.5)

7. Zhuyifan Ye, Nannan Wang, Jiantao Zhou, Defang Ouyang*. Organic crystal structure prediction via coupled generative adversarial networks and graph convolutional networks, The Innovation, 2024, 100562. (JCR Q1, IF=33.2)

8. Run Han, Zhuyifan Ye, Yunsen Zhang, Yaxin Cheng, Ying Zheng*, Defang Ouyang*. 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)

9. Nannan Wang, Yunsen Zhang, Wei Wang, Zhuyifan Ye, Hongyu Chen, Guanghui Hu, Defang Ouyang*. How can machine learning and multiscale modeling benefit ocular drug development?, Advanced Drug Delivery Reviews, 2023, 196, 114772. (JCR Q1, IF=16.1)

10. Jiayin Deng, Zhuyifan Ye, Wenwen Zheng, Jian Chen, Haoshi Gao, Zheng Wu, Ging Chan, Yongjun Wang, Dongsheng Cao, Yanqing Wang*, Simon Ming-Yuen Lee*, Defang Ouyang*. 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)

11. Wenwen Zheng, Junjun Li, Yu Wang, Zhuyifan Ye, Hao Zhong, Hung Wan Kot, Defang Ouyang*, Ging Chan*. 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)

12. Haoshi Gao, Stanislav Kan, Zhuyifan Ye, Yuchen Feng, Lei Jin, Xudong Zhang, Jiayin Deng, Ging Chan, Yuanjia Hu, Yongjun Wang, Dongsheng Cao, Yuanhui Ji, Mingtao Liang*, Haifeng Li*, Defang Ouyang*. Development of in silico methodology for siRNA lipid nanoparticle formulations, Chemical Engineering Journal, 2022, 442, 136310. (Co-first author, JCR Q1, IF=15.1)

13. Wei Wang, Shuo Feng, Zhuyifan Ye, Hanlu Gao, Jinzhong Lin*, Defang Ouyang*. 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)

14. Junjun Li, Hanlu Gao, Zhuyifan Ye, Jiayin Deng, Defang Ouyang*. 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)

15. 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)

16. Zhuyifan Ye, Wenmian Yang, Yilong Yang, Defang Ouyang*. Interpretable machine learning methods for in vitro pharmaceutical formulation development, Food Frontiers, 2021, 2, pp. 195-207. (JCR Q1, IF=9.9)

17. Wei Wang, Zhuyifan Ye, Hanlu Gao, Defang Ouyang*. 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)

18. Hanlu Gao, Wei Wang, Jie Dong, Zhuyifan Ye, Defang Ouyang*. 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)

19. Yuan He, Zhuyifan Ye, Xinyang Liu, Zhengjie Wei, Fen Qiu, Haifeng Li, Ying Zheng*, Defang Ouyang*. Can machine learning predict drug nanocrystals?, Journal of Controlled Release, 2020, 322, pp. 274–285. (Co-first author, JCR Q1, IF=10.8, Cover)

20. Haoshi Gao, Zhuyifan Ye, Jie Dong, Hanlu Gao, Hua Yu, Haifeng Li, Defang Ouyang*. Predicting drug/phospholipid complexation by the lightGBM method, Chemical Physics Letters, 2020, 747, 137354. (JCR Q3, IF=2.8)

21. Qianqian Zhao, Zhuyifan Ye, Yan Su, Defang Ouyang*. 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)

22. Run Han, Hui Xiong, Zhuyifan Ye, Yilong Yang, Tianhe Huang, Qiufang Jing, Jiahong Lu, Hao Pan, Fuzheng Ren*, Defang Ouyang*. 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)

23. Zhuyifan Ye, Yilong Yang, Xiaoshan Li, Dongsheng Cao, Defang Ouyang*. 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)

24. Yilong Yang, Zhuyifan Ye, Yan Su, Qianqian Zhao, Xiaoshan Li, Defang Ouyang*. 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)