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Abstract: Professor Charlotte Deane from the University of Oxford speaks about some of the work her research group have done on Machine Learning for Early Stage Drug Discovery to give a flavour of the different kinds of approaches they have been looking at. These run from predicting whether molecules will bind or not bind to a given protein target, to trying to remove biases from that kind of work, to finally how do we generate novel molecules in the protein binding sites.

Biography: Charlotte is Professor of Structural Bioinformatics at the Department of Statistics, University of Oxford and Deputy Executive Chair of the Engineering and Physical Sciences Research Council (EPSRC). At Oxford, Charlotte leads the Oxford Protein Informatics Group, who work on diverse problems across protein structure, interaction networks and small molecule drug discovery; combining theoretical and empirical analysis with special interest in AI. She collaborates with experimentalists in academia and industry in experiment design to leverage the power of computation for biological insight. Her work focusses on the development of novel algorithms, tools and databases that are openly available to the community. Examples include SAbDab, SAbPred, PanDDA and MEMOIR. These tools are widely used web resources and are also part of several Pharma drug discovery pipelines. Charlotte has consulted extensively with industry and 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.

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