报告时间：1月30日 上午9:00

　　会议观看地址：腾讯会议：909 679 823        

　　报告人：卢红梅教授，中南大学

　　报告摘要：

　　The predicted liquid chromatographic retention times (RTs) of small molecules are not accurate enough for wide adoption in structural identification. In this study, we used the graph neural network to predict the retention time (GNN-RT) from structures of small molecules directly without the requirement of molecular descriptors. The predicted accuracy of GNN-RT was compared with random forests (RFs), Bayesian ridge regression, convolutional neural network (CNN), and a deep-learning regression model (DLM) on a METLIN small molecule retention time (SMRT) dataset. GNN-RT achieved the highest predicting accuracy with a mean relative error of 4.9% and a median relative error of 3.2%. Furthermore, the SMRT-trained GNN-RT model can be transferred to the same type of chromatographic systems easily. The predicted RT is valuable for structural identification in complementary to tandem mass spectra and can be used to assist in the identification of compounds. The results indicate that GNNRT is a promising method to predict the RT for liquid chromatography and improve the accuracy of structural identification for small molecules.

　　报告人简介:

　　卢红梅教授，博士生导师，中南大学化学化工学院副院长，国际学术期刊Chemometrics and Intelligent Laboratory Systems编委，中国化学会计算（机）化学专业委员会委员，湖南省化学化工学会常务理事，湖南省检验检测学会常务理事；中国生物检测监测产业技术创新战略联盟理事；国家化学实验示范中心主任，国家虚拟仿真项目负责人；湖南省青年骨干教师，湖南省第十届青年联合会委员，“宝钢优秀教师奖”、中南大学“育英人才计划”入选者和“陈新民青年教师奖”获得者。先后获湖南省自然科学奖二等奖、湖南省科技进步奖三等奖、中国石油和化工自动化行业科学技术奖三等奖、怀化市科技进步奖一等奖各1项，湖南省教学成果一等奖2项。在Anal Chem、Trend Anal Chem、Metabolomics、Bioinformatics、J Chromatogr A等国际学术刊物上发表论文100余篇；参与撰写英文专著2部；主持承担科研项目20余项，其中国家自然科学基金6项。

　　报告联系人：生物技术研究部1808组 许国旺

　　联系电话：0411-84379530