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 have published 20 papers in SCI journals, with 17 appearing in JCR Q1 journals. I have been the first or co-first author on 12 of these papers, one of which received the "Sixth Chinese Association for Science and Technology Outstanding Scientific Paper" award. My H-index is 14.

Highlights

• We are currently accepting applications from prospective Ph.D. students for the year 2025. 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

Dr. Ye's research interests include: machine learning methods for organic crystal structure prediction, machine learning methods for organic solubility prediction, interpretable machine learning approaches for biomedical tabular data, machine learning methods for pharmacokinetic parameters prediction, 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 methods and machine learning in biomedicine.

Publications

1. Zheng Wu, Nannan Wang, Zhuyifan Ye, et al. 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)

2. 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, 2024. (JCR Q2, IF=3.7)

3. 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)

4. 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)

5. 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)

6. 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)

7. 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)

8. 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)

9. 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)

10. 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)

11. 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)

12. 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)

13. 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)

14. 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)

15. 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)

16. 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)

17. 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)

18. 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)

19. 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)

20. 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)