时间：2025年4月21日14:00  
地点：犀浦3号教学楼X31541报告厅

主讲人：Min Wu  博士

内容简介：

The drug discovery pipeline is a complex, multi-stage process encompassing target identification, hit discovery, lead optimization, and clinical development. While traditional approaches are often time-consuming and costly, artificial intelligence (AI) has emerged as a transformative tool to accelerate these stages. In this talk, we will explore how AI-driven methods can enhance two critical phases of drug discovery: target identification via synthetic lethality (SL) prediction and hit discovery via RNA-small molecule binding affinity prediction. I will present our recent work on benchmarking machine learning algorithms for SL prediction. In this work, we systematically benchmarked 12 recent machine learning methods for SL prediction, assessing their performance across diverse data splitting scenarios, negative sample ratios, and negative sampling techniques, on both classification and ranking tasks. I will also introduce our deep learning framework called DeepRSMA for predicting RNA-small molecule binding affinities. Our method demonstrates superior performance over existing tools, enabling the virtual screening of RNA-targeted ligands with high accuracy.

主讲人简介：

 Dr. Min Wu is currently a Principal Scientist at Institute for Infocomm Research (I2R), Agency for Science, Technology and Research (A*STAR), Singapore. He received his Ph.D. degree in Computer Science from Nanyang Technological University (NTU), Singapore, in 2011 and B.E. degree in Computer Science from University of Science and Technology of China (USTC) in 2006. He received the best paper awards in EMBS Society 2023, IEEE ICIEA 2022, IEEE SmartCity 2022, InCoB 2016 and DASFAA 2015. He also won the CVPR UG2+ challenge in 2021 and the IJCAI competition on repeated buyers prediction in 2015. He has been serving as an Associate Editor for journals like Neurocomputing, Neural Networks and IEEE Transactions on Cognitive and Developmental Systems, as well as conference area chairs of leading AI and machine learning conferences, such as ICLR, NeurIPS, KDD, etc. His current research interests focus on AI and machine learning for time series data, graph data, and biological and healthcare data.