Email

defangouyang@um.edu.mo

Tel

(853) 8822-4514

Office

E12-1041

Consultation Hours

Tue: 9:00~10:00
Thu: 16:00~17:00

De Fang OUYANG 歐陽德方

Associate Professor

Academic Qualifications

  • PhD,  The University of Queensland, Australia
  • MSc, Shenyang Pharmaceutical University, China
  • BSc, Shenyang Pharmaceutical University, China

Teaching

HSCI4011Pharmacoinformatics
CMED728Computational Pharmacy
CMED724Application of Pharmacokinetics and Metabonomics in Drug Development

Research Interests

Dr Ouyang has a multidisciplinary background in pharmaceutics & computer modelling, with experience in academia and industry. He obtained his bachelor (2000) and master (2005) in pharmaceutics from Shenyang Pharmaceutical University, China. He completed his PhD in pharmacy at The University of Queensland, Australia, in 2010 and progressed directly to his faculty position (Lecturer in Pharmaceutics, PI) at Aston University (UK). From the end of 2014, he moved to the University of Macau.

Since 2011, he has pioneered the integration of multi-scale modeling, artificial intelligence and big data techniques in the field of drug delivery – “computational pharmaceutics“. He has published 2 books, 5 book chapters and over 60 refereed SCI journal papers. He held 6 approved patents, which had been used in medicinal products. He edited the first book “Computational Pharmaceutics – the application of molecular modeling in drug delivery” (John Wiley & Sons Inc., 2015) in this research area. He is invited to be the Editors-in-Chief of “In Silico Pharmacology” (Springer Nature) and associate editor of “Drug Delivery and Translational Research”. He also serves as the editorial board or scientific advisor of “Asian Journal of Pharmaceutical Sciences”, “Pharmaceutical Research”, “Pharmaceutics” and “Journal of Pharmaceutical Sciences”. He is establishing the first global artificial intelligence (AI)-based formulation platform. He successfully trained 4 PhD and 25 master. Currently his group includes 1 postdoctoral, 3 PhD students and 4 master students.

His research focused on computational pharmaceutics, including:

  • Artificial intelligence (AI) of pharmaceutical formulations: to build the database of pharmaceutical formulations and predict pharmaceutical formulations by machine learning approaches;
  • Multi-scale modeling in drug delivery: to integrate quantum mechanics (QM), molecular dynamics (MD) and physiologically based pharmacokinetic (PBPK) modeling into drug delivery systems;
  • Pharmacoinformatics: big data analysis of pharmaceutical information from the literature, patent, clinical trial and marketed products.

10 representative publications in recent 3 years

  1. Gao Hanlu, Wang Wei, Dong Jie, Ye Zhuyifan, 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, 158 (2021) 336–346;
  2. Yuan He, Zhuyifan Ye, Xinyang Liu, Hai-Feng Li, Ying Zheng, Defang Ouyang*. Can machine learning predict drug nanocrystals? Journal of Controlled Release, 2020, 322, 274-285; (Cover page)
  3. 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, 16-25; (Cover page)
  4. Qianqian Zhao, Ye Zhuyifang, 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), 1241-1252;
  5. Conglian Yang, Kun Tu, Hanlu Gao, Liao Zhang, Yu Sun, Ting Yang, Li Kong, Defang Ouyang*, Zhiping Zhang*. The novel platinum(IV) prodrug with self-assembly property and structure-transformable character against triple-negative breast cancer, Biomaterials, 2020, 232, 119751;
  6. 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), 533−541;
  7. 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):177–185;
  8. Hao Zhong, Ging Chan, Hao Hu, Yuanjia Hu, Defang Ouyang*. A comprehensive map on FDA-approved pharmaceutical products. Pharmaceutics, 2018, 10(4), 263;
  9. Tianhe Huang, Qianqian Zhao, Yan Su, Defang Ouyang*. Investigation of Molecular Aggregation Mechanism of Glipizide/Cyclodextrin Complexation by Combined Experimental and Molecular Modeling Approaches. Asian Journal of Pharmaceutical Sciences, 2019, 14(6), 609-620; (cover page)
  10. Weixiang Zhang, Qianqian Zhao, Junling Deng, Yuanjia Hu, Yitao Wang, Defang Ouyang*. Big data analysis of global advances in pharmaceutics and drug delivery from 1980 – 2014. Drug Discovery Today. 2017, 22(8), 1201-1208.

  • Editorial-in-Chief of “In Silico Pharmacology” (Springer);
  • Associate editor of “Drug Delivery and Translational Research”;
  • Editorial board of “Asian Journal of Pharmaceutical Sciences”, “Pharmaceutical Research” and “Pharmaceutics”;
  • Scientific Advisor of “Journal of Pharmaceutical Sciences”;
  • Topic editor of “Pharmaceutics” and “Frontier in Pharmacology”;
  • Fellow of the Higher Education Academy (UK);
  • Grant reviewer of BBSRC (UK) and French National Research Agency (ANR);
  • 2015 Outstanding reviewer of “International Journal of Pharmaceutics”;
  • Reviewer of over 20 SCI journals;

  • Computational Pharmacy Society (CPS)
  • American Chemical Society (ACS)
  • Asian Association of Schools of Pharmacy (AASP)