COLLOQUIUM: Jian Tang, "Geometric Deep Learning for Drug Discovery"
From Erin Klapacz
Abstract: Drug discovery is a very long and expensive process, taking on average more than 10 years and costing $2.5B to develop a new drug. Artificial intelligence has the potential to significantly accelerate the process of drug discovery by extracting evidence from a huge amount of biomedical data and hence revolutionizes the entire pharmaceutical industry. In particular, graph representation learning and geometric deep learning--a fast growing topic in the machine learning and data mining community focusing on deep learning for graph-structured and 3D data---has seen great opportunities for drug discovery as many data in the domain are represented as graphs or 3D structures (e.g. molecules, proteins, biomedical knowledge graphs). In this talk, I will introduce our recent progress on geometric deep learning for drug discovery and also a newly released open-source machine learning platform for drug discovery, called TorchDrug.
Bio: Jian Tang is currently an assistant professor at Mila-Quebec AI Institute and also at Computer Science Department and Business School of University of Montreal. He is a Canada CIFAR AI Research Chair. His main research interests are geometric deep learning, deep generative models, knowledge graphs and drug discovery. During his PhD, he was awarded with the best paper at ICML2014; in 2016, he was nominated for the best paper award in World Wide Web (WWW); in 2020, he is awarded with Amazon and Tencent Faculty Research Award. He has published a set of representative works in the field of graph representation learning, such as LINE and RotatE. His work on node representation learning, LINE, has been widely recognized and is the most cited paper at the WWW conference between 2015 and 2019. Recently, his group just released an open-source machine learning package, called TorchDrug, aiming at making AI drug discovery software and libraries freely available to the research community. He is also an area chair of ICML and NeurIPS.