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Drug discovery with machine learning: Towards a new way of thinking about molecules
16 October 2018 | 18:00 - 20:00
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This event is held in the Pfizer lecture theatre in the Cambridge University Chemistry Department and will be followed with a wine and cheese reception.
“Drug discovery is akin to finding a needle in a haystack: There are, theoretically, around 1 million million stable organic molecules. However, out of those theoretically stable molecules, we only know how to make around 0.1% them, and it is experimentally infeasible to test every molecule that we know how to make. In my talk, I will discuss machine learning algorithms that can predict which molecules are likely to be functional against medically physiological processes by learning from the published chemical literature, as well as predicting ways to make those active molecules from readily available starting materials. I will show that the design of machine learning algorithms invites us to think about chemistry and molecules in a fundamentally different way.
Dr Lee is a Winton Advanced Fellow at the University of Cambridge. He was previously a 2017 George F. Carrier Fellow and 2016 Fulbright Scholar at Harvard University after his PhD at the Mathematical Institute, University of Oxford. His research is is broadly speaking in statistical physics and soft condensed matter, with a particular focus on integrating physics, statistics and machine learning.”
“Drug discovery is akin to finding a needle in a haystack: There are, theoretically, around 1 million million stable organic molecules. However, out of those theoretically stable molecules, we only know how to make around 0.1% them, and it is experimentally infeasible to test every molecule that we know how to make. In my talk, I will discuss machine learning algorithms that can predict which molecules are likely to be functional against medically physiological processes by learning from the published chemical literature, as well as predicting ways to make those active molecules from readily available starting materials. I will show that the design of machine learning algorithms invites us to think about chemistry and molecules in a fundamentally different way.
Dr Lee is a Winton Advanced Fellow at the University of Cambridge. He was previously a 2017 George F. Carrier Fellow and 2016 Fulbright Scholar at Harvard University after his PhD at the Mathematical Institute, University of Oxford. His research is is broadly speaking in statistical physics and soft condensed matter, with a particular focus on integrating physics, statistics and machine learning.”
