Abstract:

Deep learning methods applied to chemistry can be used to accelerate the discovery of new molecules, such as promising pharmaceuticals. Notably, methods such as graph neural networks (GNNs) are interesting tools to explore for molecular design because graphs are natural data structures for describing molecules. The process of designing novel, drug-like compounds can be viewed as one of generating graphs which optimize all the features of the desirable molecules.

In this talk, I will provide an overview of how deep learning methods can be applied to complex drug design tasks, focusing on our recently published tool, GraphINVENT. GraphINVENT uses GNNs and a tiered deep neural network architecture to probabilistically generate new molecules a single bond at a time, and learns to build new molecules resembling a training set without any explicit programming of chemical rules. GraphINVENT is one of many recent platforms which aim to streamline the drug discovery process using AI.

Biography:

I joined the Molecular AI group at AstraZeneca in October 2018. My work focuses on using deep learning methods for graph-based molecular design. Before AstraZeneca, I was a PhD student in Professor Berend Smit’s molecular simulation group at UC Berkeley and EPFL. I received my PhD in Chemistry from UC Berkeley in July 2018, and my BS in Chemistry from Caltech in June 2013.