RNA-targeted Drug Discovery: RNA-Small Molecule Binding Prediction

Speaker:  Min Wu, Institute for Infocomm Research (I2R), Agency for Science, Technology and Research (A*STAR), Singapore.

Time:       10:00 am, Jun. 6th.

Location:SIST 1A-200

Host:       Jie Zheng

Abstract:

The drug discovery pipeline is a complex, multi-stage process encompassing target identification, hit discovery, lead optimization, and clinical development. While traditional drugs mainly target proteins, RNA-targeted drug discovery focuses on developing therapeutics that directly interact with RNA molecules. In this talk, we use the hit discovery stage as an example and introduce deep learning techniques designed for RNA–small molecule binding affinity and binding site prediction. DeepRSMA is a cross-attention-based deep learning model for predicting RNA–small molecule binding affinity. It employs nucleotide- and atomic-level feature extraction modules for RNA and small molecules, respectively, and uses a transformer-based cross-fusion module to model their interactions. The final prediction integrates features from both the extraction and fusion modules. MVRBind is a multi-view, multi-scale graph convolutional network for predicting RNA–small molecule binding sites. It captures RNA features at the primary, secondary, and tertiary structural levels and uses a fusion module to combine multi-scale representations into a unified embedding for accurate site prediction. Together, these models highlight the potential of deep learning to advance RNA-targeted drug discovery by improving the identification of promising RNA–small molecule interactions.

Bio:

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.