Seminar - by Dr. Qingpeng Zhang
Host: Centre of Cancer Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, HKU
Co-host: HKU Musketeers Foundation Institute of Data Science
Speaker: Dr. Qingpeng Zhang, Associate Professor, HKU IDS / Department of Pharmacology and Pharmacy
Date: November 28, 2023 (Tuesday)
Time: 1:00pm – 2:00pm
Venue: 3SR-SR3, Room 402, 4/F Academic Building, 3 Sassoon Road, Pokfulam (Capacity: 50 – No Registration Required)
Drug discovery is a challenging and costly process that requires a deep understanding of the mechanism of drug action (MODA), which is how a drug affects the biological system at the molecular level. In this talk, I will present our recent studies on using a network-based machine learning approach to characterize MODA by analyzing a comprehensive biological network that captures the complex high-dimensional molecular interactions between genes, proteins and chemicals. I will show that our methods outperform state-of-the-art machine learning baselines in predicting MODA. I will also demonstrate that our methods can identify explicit critical paths that are consistent with clinical evidence, and explain how these paths reveal the underlying biological mechanisms of drug action. Our research provides a novel interpretable artificial intelligence perspective on drug discovery, and has the potential to facilitate the development of new and effective drugs.
Dr. Qingpeng Zhang is an Associate Professor at The University of Hong Kong (HKU), affiliated with the Musketeers Foundation Institute of Data Science and the Department of Pharmacology and Pharmacy. He joined HKU in August 2023, after serving as an Associate Professor at the School of Data Science of The City University of Hong Kong (CityU). He obtained his Ph.D. degree in Systems and Industrial Engineering from the University of Arizona and conducted his postdoctoral research in the Tetherless World Constellation, Department of Computer Science at Rensselaer Polytechnic Institute. He is a senior member of IEEE, and an associate editor for BMJ Mental Health, IEEE TITS, and IEEE TCSS.
His research focuses on medical informatics, AI in drug discovery, healthcare data analytics and network science. He has published in top journals such as Nature Human Behaviour, Nature Communications, PNAS, JAMIA and MIS Quarterly, and his work has been featured in media outlets such as The Washington Post, The New York Times, New York Public Radio, The Guardian and Ming Pao. He has received several awards for his research excellence, including The President’s Award (2022) and the Outstanding Research Award (2021) from CityU and the Andrew P. Sage Best Transactions Paper Award (2021) from IEEE Systems, Man, and Cybernetics Society.
For information, please contact: