3.1 AI-driven pharmacy and pharmaceutical innovation
Tracks
Track 1
| Thursday, December 4, 2025 |
| 3:45 PM - 5:00 PM |
| Law Annex Seminar Room 104 (F10A.01.104) |
Speaker
Assoc Prof Defang Ouyang
University Of Macau
Invited speaker presentation: Challenges and Opportunities of Artificial Intelligence in Drug Delivery
3:45 PM - 4:00 PMBiography
Dr. Defang Ouyang's research centers on computational pharmaceutics, combining artificial intelligence and multi-scale modeling to enhance drug delivery and pharmaceutical formulations.
博士 Xinyang LIU
University of Macau
Enhancing mRNA-lipid Nanoparticle Prediction via the Chemical Language Model and Multi-task Learning
4:00 PM - 4:10 PMBiography
Xinyang Liu is a Ph.D. candidate at the Institute of Chinese Medical Sciences (ICMS), University of Macau, Macau, China. Her research focuses on physiologically based pharmacokinetic (PBPK) modeling, pharmacokinetics/pharmacodynamics (PK/PD), and individualized drug therapy, particularly in the context of pregnancy-induced hypertension. With a background as a clinical pharmacist, she brings valuable real-world clinical insight into her research, bridging the gap between computational pharmacology and patient care. Her current work integrates modeling approaches with clinical parameters to support precision dosing strategies in vulnerable populations such as pregnant women. She is actively engaged in interdisciplinary collaboration, combining pharmacology, bioinformatics, and maternal-fetal medicine. Xinyang Liu is dedicated to improving the safety and efficacy of pharmacotherapy during pregnancy, and her work contributes to the development of more personalized and evidence-based treatment protocols. She continues to explore the translational potential of model-informed drug development in clinical settings.
Dr Nguyen Minh Thai
Principal Lecturer
Faculty of Pharmacy, University of Medicine and Pharmacy at Ho Chi Minh City
Successful Sybodies Immobilization On Sams Via An Innovative QCM Biosensing Approach
4:10 PM - 4:20 PMBiography
Dedicated and self-motivated researcher with 10+years with experience of
recombinant protein expression in E. coli and optimizing bioprocess.
Developed a total of 5 bioproduct formulations and bioprocess protocols.
Collaborated on 5+ projects of probiotic development. Contributed to developing
the cell machine interface to control bioprocessing and published 14+ article in
bioengineering and biopharmaceutical topic.
Prof Ying Zheng
Head (department Of Pharmaceutical Sciences)
University of Macau
A Multi-scale Prediction Model Using Machine Learning and Dynamics Simulation for the Selection of Transdermal Penetration Enhancers
4:20 PM - 4:30 PMBiography
Dr. Zheng Ying is a Professor at the State Key Laboratory of Chinese Medicine Quality Research, Institute of Chinese Medical Sciences, University of Macau, and Head of the Department of Pharmaceutical Sciences, Faculty of Health Sciences, University of Macau. Her research interests include novel drug delivery systems and their in vivo fate studies, including oral, transdermal and pulmonary delivery of insoluble ingredients; construction of zebrafish models to visualize the in vivo fate of nanomedicines, tumor targeting and immune effects; and machine-learning screening of transdermal enhancers and their experimental validation. She has hosted and participated in the Overseas Research Fund for Overseas Scholars of Natural Science Foundation of Hong Kong, Hong Kong and Macao, Macao Science and Technology Development Fund, and Erasmus Mundus Collaborative Research Program in Europe. She serves as an editorial board member of Pharmaceutical Research and Asian Journal of Pharmaceutical Sciences.
博士 Xinyang LIU
University of Macau
Physics Informed One-stop AI Preformulation Prediction Platform
4:30 PM - 4:40 PMBiography
Xinyang Liu is a Ph.D. candidate at the Institute of Chinese Medical Sciences (ICMS), University of Macau, Macau, China. Her research focuses on physiologically based pharmacokinetic (PBPK) modeling, pharmacokinetics/pharmacodynamics (PK/PD), and individualized drug therapy, particularly in the context of pregnancy-induced hypertension. With a background as a clinical pharmacist, she brings valuable real-world clinical insight into her research, bridging the gap between computational pharmacology and patient care. Her current work integrates modeling approaches with clinical parameters to support precision dosing strategies in vulnerable populations such as pregnant women. She is actively engaged in interdisciplinary collaboration, combining pharmacology, bioinformatics, and maternal-fetal medicine. Xinyang Liu is dedicated to improving the safety and efficacy of pharmacotherapy during pregnancy, and her work contributes to the development of more personalized and evidence-based treatment protocols. She continues to explore the translational potential of model-informed drug development in clinical settings.
Prof Qi Zhao
University of Macau
Artificial Intelligence-driven Design of Humanized Antibodies for immunotherapy
4:40 PM - 4:50 PMBiography
Prof. Qi ZHAO obtained his PhD from the Department of Biochemistry at the Chinese University of Hong Kong. He conducted postdoctoral research at the National Cancer Institute (NCI) of the National Institutes of Health (NIH) in the United States and at the Memorial Sloan Kettering Cancer Center. He has served at the Faculty of Health Sciences, University of Macau since 2016. In recent years, he has published over 120 papers in journals such as Nature Communications, Leukemia, Journal of Hematology & Oncology, Cell Reports Medicine, Advanced Sciences, and Clinical Cancer Research. He has received the NIH Federal Technology Transfer Award and the Best Research Award of the university. He has led various research projects (total 17) and numerous industrial collaboration projects (Novo Nordisk, China Resources Pharm). He has filed 20 patents and completed the industrial translation of two antibody drugs. His research mainly focuses on antibody immunotherapy and drug delivery.
Mr Ziyang Sun
Nanjing University of Chinese Medicine
AI‑Guided Identification of Microbial Signatures Linking Androgen Modulation to Accelerated Diabetic Wound Healing
4:50 PM - 5:00 PMBiography
Ziyang Sun is a graduate student at the Department of Burns and Plastic Surgery, Nanjing Drum Tower Hospital, Clinical College, Nanjing University of Chinese Medicine. His research focuses on host–microbiome–immune interactions in chronic wound healing, with a particular interest in androgen modulation and precision microbiota-targeted therapies. He has experience in diabetic wound models, microbial sequencing (16S rRNA), immunohistochemistry, and transcriptomic profiling. Ziyang is currently exploring AI-driven methods—such as LASSO, random forest, and SHAP—for identifying predictive microbial signatures that influence healing trajectories. He is also engaged in interdisciplinary work combining endocrinology, bioinformatics, and wound biology. His goal is to uncover novel therapeutic strategies that integrate hormonal regulation and microbiome engineering to improve outcomes in patients with non-healing wounds.