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Charlotte Deane

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About The Speaker

Charlotte Deane

Charlotte is Professor of Structural Bioinformatics in the Department of Statistics at the University of Oxford and co-director of the Systems Approaches to Biomedical Research Centre for Doctoral Training which she founded in 2009. In January 2024, Charlotte became Executive Chair of the Engineering and Physical Sciences Research Council (EPSRC). From 2022 to 2023, Charlotte was Chief AI Officer at Exscientia, a biotech with ~450 employees, where she led its computational scientific development.

She has held numerous senior roles at the University of Oxford including Head of the Department of Statistics and Deputy Head of the Mathematical, Physical and Life Sciences (MPLS) division. She was the Deputy Executive Chair of the UK’s Engineering and Physical Sciences Research Council from 2019 to 2021. She served on SAGE, the UK Government’s Scientific Advisory Group for Emergencies, during the COVID-19 pandemic, and acted as UK Research and Innovation’s COVID-19 Response Director.

She was appointed Member of the Order of the British Empire (MBE) in the 2022 Birthday Honours for services to COVID-19 research. At Oxford, Charlotte leads the Oxford Protein Informatics Group (OPIG), who work on diverse problems across immunoinformatics, protein structure and small molecule drug discovery; using statistics, AI and computation to generate biological and medical insight. Her work focuses on the development of novel algorithms, tools and databases that are openly available to the community. These tools are widely used web resources and are also part of several Pharma drug discovery pipelines. Charlotte is on several advisory boards and has consulted extensively with industry. She has set up a consulting arm within her own research group as a way of promoting industrial interaction and use of the group’s software tools.

Moving the dial on computational antibody design

Antibodies play a key role in the immune system and our response to vaccines and have shown great promise as biotherapeutics. The development of new biotherapeutics typically takes many years and requires over $1bn in investment. Computational methods and in particular, machine learning, have shown great promise for increasing the speed and reducing the cost of biotherapeutic development. In this talk I will describe some of the novel computational tools and databases we are pioneering in biotherapeutics from accurate rapid structure prediction to the prediction of their affinity and binding, looking at both their promise and limitations.

The Antibody Series
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