我们的GNN课程邀请到了加拿大蒙特利尔学习算法研究所(MILA)唐建教授讲授用几何深度学习进行药物发现的报告！
主题: Geometric Deep learning for Drug Discovery
讲师: Jian Tang (www.jian-tang.com) is an associate professor at Mila (Quebec AI Institute founded by Yoshua Bengio) and HEC Montreal.
摘要: 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.