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'Science' publishes new paper on Inferring the effectiveness of government interventions against COVID-19

16 Dec 2020

Researchers at Oxford have found that limiting gatherings to fewer than 10 people and closing educational institutions were among the most effective non-pharmaceutical interventions at suppressing transmission of COVID-19.  Professor Yee Whye Teh from the Department of Statistics contributed to the paper, which was co-lead by Mrinank Sharma, who is affiliated with the Department.

The paper, Inferring the effectiveness of government interventions against COVID-19, was published in Science on Tuesday 15th December.  Full details can be found here.

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Oxford Statistics at the forefront of AI-driven drug discovery

10 Jun 2025

Professor Charlotte Deane from the Department of Statistics has been announced as a senior principal investigator on a £8 million government-backed consortium that will create the world's largest dataset for AI-driven drug discovery.

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PoseBusters: AI-based docking methods fail to generate physically valid poses or generalise to novel sequences

28 Apr 2025

Predicting how small molecules (or “ligands”) bind to proteins and other macromolecules like DNA and RNA is an important part of computer-aided drug discovery. Oxford Protein Informatics Group DPhil Student, Martin Buttenschoen, Professor Charlotte Deane and Professor Garrett M. Morris recently examined how well deep learning-based methods can dock a ligand into a protein pocket.

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