A Family of Pretrained AI Models for Target Discovery and Drug Design

[CFAR Distinguished Professor Lecture Series]
A Family of Pretrained AI Models for Target Discovery and Drug Design (Hybrid event) by Dr Song Le
4 Aug 2023 | 3.00pm (Singapore Time)

How can we leverage large amounts of unsupervised data models to accelerate target discovery and drug design? In this talk, Dr. Song Le from BioMap and Mohamed bin Zayed University of AI (MBZUAI) will introduce the xTrimo family of large-scale and interrelated pre-trained models across a multiscale of biological processes, integrating a huge amount of data from protein sequences, structures, protein-protein interactions and single-cell transcriptomics data. These pretrained models could be used downstream to address many problems arising from target discovery and drug design. Dr. Song will then conclude the talk by illustrating examples, to show that xTrimo models may achieve State-Of-The-Art (SOTA) in drug target combination and antigen-antibody complex structure predictions.

Dr. Song Le
Chief AI Scientist, BioMap
Professor & Department Chair,
Mohamed bin Zayed University of AI (MBZUAI)
Dr. Song Le is a deep learning and graph neural network expert and has extensive experience in AI technology and engineering. He is now the Chief Technology Officer and Chief AI Scientist at BioMap, where he leads the efforts in strategic planning and technological development of xTrimo - a large-scale model for life science and constructs the high-throughput, closed-looped system to complement the AI engine. Prior to joining BioMap, Dr. Song was a Professor and the Department Chair of Machine Learning at MBZUAI, an Associate Professor in the College of Computing and the Associate Director of the Machine Learning Center at the Georgia Institute of Technology. He was also the Head of the Deep Learning team at Ant Financial and a researcher at the Alibaba DAMO Academy and Google. Since 2008, Dr. Song started to work on AI for life science problems when he was a post doc at Carnegie Mellon University and has done a series of impactful work related to target discovery and drug design using machine learning methods. His work has earned him numerous Best Paper Awards in major AI conferences such as NeurIPS, ICML and AISTATS, to name a few. Dr. Song is also a board member of ICML and the program chair for ICML 2022.