Zhuyifan Ye, Ph.D., is a lecturer (PI) at the Faculty of Applied Sciences, Macau Polytechnic University, specializing in the application of artificial intelligence to address challenges in pharmaceutics and drug discovery. He obtained his Bachelor's degree from China Pharmaceutical University in 2016, and his Master's and Ph.D. degrees from the University of Macau in 2018 and 2022, respectively. Since 2023, he has been a part of the Artificial Intelligence Drug Discovery Center at Macau Polytechnic University. His research areas include crystal structure prediction, the application of advanced machine learning algorithms, handling small imbalanced datasets, pharmacokinetic parameter prediction, reverse engineering of drugs using generative and discriminative models, and organic solubility prediction. To date, he has published 18 papers in SCI journals, 16 of which appeared in JCR Q1 journals. He has been the first or co-first author of 12 papers, one of which won the "Sixth Chinese Association for Science and Technology Outstanding Scientific Paper" award.

Highlights

• We are seeking self-motivated Ph.D. students to collaborate on exciting projects related to AI-driven drug development. Join our team and contribute to cutting-edge research in this field.

• 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, which should include details such as your publications, ranking/GPA, English score, and any other relevant information.

• MPU students are encouraged to connect with us if you are interested in conducting research in our laboratory. We welcome your participation and look forward to hearing from you.

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

Prospective Students

We are currently accepting applications from prospective Ph.D. students for the year 2024. We are offering scholarships, covering tuition fees and providing a living allowance for successful Ph.D. applicants.

Additionally, 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). This provides an opportunity to engage in cutting-edge research projects and explore the fascinating realm of pharmaceutical sciences.

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 pharmaceutics and drug discovery.

Publications

1. 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=33.1)

2. 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)

3. 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)

4. 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)

5. 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)

6. 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)

7. 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)

8. 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)

9. 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)

10. 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)

11. 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)

12. 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)

13. 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)

14. 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)

15. 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)

16. 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)

17. 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)

18. 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)