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Accelerating medicinal chemistry with hypothesis-driven machine learning
The ongoing efforts in COVID antiviral discovery is a stark reminder that small molecule drug discovery is still painfully slow. This is partly because the medicinal chemistry optimisation cycle – designing molecules, synthesising molecules, and feeding data from biological assays into the next round of designs – is still empirically driven. In my talk, I will discuss our progress towards using hypothesis-driven machine learning to close the design-make-test cycle: predicting molecular properties, designing optimised molecules and ensuring the designed molecules are rapidly synthesizable. I will show how physical and chemical understanding can be incorporated into machine learning, enabling data-driven methods to be useful in the low-data limit that most drug discovery campaigns operate in. I will illustrate our approach using examples from COVID Moonshot, an open science drug discovery project that aims to discover oral SARS-CoV-2 main protease inhibitors.
Date:
26 May 2021, 13:00
Venue:
https://zoom.us/j/99641298801?pwd=YkFkWm9lK3ArU2hrZUl4YVE0RXdEUT09
Speaker:
Dr Alpha Lee (University of Cambridge)
Organising department:
Structural Genomics Consortium
Organiser:
Charlotte Morgan (University of Oxford)
Part of:
CMD Seminars
Booking required?:
Not required
Audience:
Public
Editor:
Charlotte Morgan
