Wengong Jin
Broad Institute of MIT and Harvard
Molecules and proteins are geometric objects and their function relies on their structure (e.g. graph or 3D point cloud). The challenge of AI-driven molecular design includes prediction and generation. The prediction task (forward problem) aims to predict the property of a molecule/protein automatically based on its structure. The generation task (inverse problem) aims to generate molecules/proteins that have specific properties of interest. In this talk, I will present how to use geometric/graph deep learning to accelerate molecular design. The first half of the talk will focus on small molecule drug discovery, i.e. how to build graph convolutional networks for property prediction and fragment-based generative models for the de novo drug design. The second half of the talk will focus on antibody engineering, i.e., how to build equivariant geometric deep learning models to dock antibodies onto an antigen epitope and generate CDR sequences that bind to the epitope.
Wengong Jin is a Postdoctoral Associate at Eric and Wendy Schmidt Center of Broad Institute. He finished his Ph.D. in MIT CSAIL, advised by Regina Barzilay and Tommi Jaakkola . His research seeks to develop novel machine learning algorithms for biology, including drug discovery, immunology, genetic engineering, and synthetic biology. He is particularly interested in deep generative models and graph neural networks.
