Discovering New Molecules Using Graph Neural Networks by Rocío Mercado

There is growing interest in graph neural networks (GNNs) for graph representation learning. This is because graphs are natural data structures for describing an assortment of relational information, including molecular structures. One of the topics we are interested in within the Molecular AI (MAI) group at AstraZeneca is using GNNs for the design and discovery of new drug molecules.
The process of designing novel, drug-like molecules can be viewed as one of generating graphs that optimize all the features of the desirable molecules. We are interested in graph-based methods because they have the potential to capture a lot of molecular information with greater flexibility. Our generative models can quickly learn the underlying distribution of properties in training set molecules without any explicit writing of chemical rules.
In this talk, Rocío demonstrates how we can use deep learning methods such as GNNs to carry out pharmaceutical drug discovery more efficiently.
Rocío 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.
Rocío presented at the 2020 GAIA Conference, held online from Svenska Mässan, on the 27th of November, 2020.

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