100元2小时不限次数电话号码,全国空降200元快餐联系方式,24小时微信快餐妹,全国同城约资源匹配系统

科学研究
学术报告
当前位置: 学院主页 > 科学研究 > 学术报告 > 正文

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

发布时间：2025-06-10 作者： 浏览次数：
Speaker: 吴敏 DateTime: 2025年6月16日（周一）上午10:00
Brief Introduction to Speaker:

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.


 Place: 国交2号楼201会议室
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 capture...
主站蜘蛛池模板： 申扎县| 鹰潭市| 蓝田县| 龙江县| 淳安县| 涪陵区| 德安县| 阿城市| 田阳县| 台南县| 合作市| 云阳县| 邹城市| 申扎县| 河南省| 土默特左旗| 贵定县| 济宁市| 石城县| 平陆县| 承德县| 东丰县| 乌恰县| 民县| 宝鸡市| 铜川市| 横峰县| 达孜县| 新乡市| 阳谷县| 陵水| 社会| 赤壁市| 普定县| 汾西县| 阳信县| 连云港市| 绥江县| 图木舒克市| 滦平县| 东乌珠穆沁旗|