内容简介：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.

报告人：张清鹏

报告人简介：香港大学同心基金数据科学研究院和医学院药理及药剂学系副教授，曾任教于香港城市大学数据科学学院。他在亚利桑那大学获得系统工业工程博士学位，本科毕业于华中科技大学自动化专业，在伦斯勒理工学院计算机系从事博士后研究。张博士长期致力于基于网络化知识的精准医学研究，并在药物发现、公共卫生和健康管理等领域取得成功应用。张博士的研究成果发表在Nature Human Behaviour、Nature Communications、PNAS、Proceedings of the Royal Society A、MIS Quarterly等期刊上，并获得华盛顿邮报、纽约时报、卫报等中外媒体的广泛报道。张博士是IEEE Senior Member和Royal Society of Medicine Fellow，并担任npj Digital Medicine, BMJ Mental Health, INFORMS Journal on Data Science, IEEE TCSS和IEEE TITS编委。

时  间：2024年8月31日（周六）下午14：00开始

地  点：腾讯会议号54238559950

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信息科学技术学院

2024年8月23日