Dr. Zhang Qingpeng, Associate Professor at the University of Hong Kong, delivered an academic lecture titled "Biology-Inspired Machine Learning Approach for Characterizing Drug Mechanisms" at our university
On the morning of May 2, 2024, Dr. Zhang Qingpeng, an associate professor at the University of Hong Kong, delivered an academic lecture titled "Biology-Inspired Machine Learning Approach for Characterizing Drug Mechanisms" at the N52 Conference Room in the Wui Chi Building. The lecture was hosted by Professor Henry Tong, who serves as the director of the Centre for Artificial Intelligence Drug Discovery.
Professor Tong began by introducing Dr. Zhang Qingpeng's academic background. Dr. Zhang joined the University of Hong Kong in August 2023, previously holding the position of associate professor at the School of Data Science at City University of Hong Kong. He earned his Ph.D. in Systems and Industrial Engineering from the University of Arizona and conducted postdoctoral research at the Computer Science Department of Rensselaer Polytechnic Institute. Additionally, Dr. Zhang is a senior member of the IEEE and serves as an associate editor for BMJ Mental Health, IEEE TITS, and IEEE TCSS.
During the presentation, Associate Professor Zhang Qingpeng highlighted the latest research findings of his team, focusing on a network-based machine learning approach to characterize the mechanism of drug action (MODA). This approach involves analyzing a comprehensive biological network that captures complex high-dimensional molecular interactions among genes, proteins, and chemicals. Dr. Zhang demonstrated how this method surpasses current leading machine learning benchmarks in predicting MODA and can pinpoint key pathways that align with clinical evidence, shedding light on the underlying biological mechanisms of drug action. Furthermore, Dr. Zhang discussed some of the challenges encountered in their ongoing research, such as enhancing model interpretability and developing more explanatory deep neural network models inspired by biology, like BioXNet.
The lecture provided a stimulating academic experience for the faculty and students in attendance. Participants acknowledged that drug discovery is a complex and expensive undertaking that necessitates a profound understanding of drug mechanisms. Dr. Zhang Qingpeng's team's research offers a fresh, interpretable artificial intelligence perspective for drug discovery, which is anticipated to advance the exploration and development of new medications. This study marks a significant advancement in pharmacology and offers robust support for future drug research and development endeavors.