Liquid State Theory Meets Deep Learning and Molecular Informatics

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• Dr Alpha Lee, University of Cambridge, Department of Physics
• Wednesday 25 October 2017, 14:15-15:15
• Department of Chemistry, Cambridge, Unilever lecture theatre.

If you have a question about this talk, please contact Lisa Masters.

A large class of problems in machine learning pertains to making sense of high dimensional and unlabelled data. The challenge lies in separating direct variable-variable interactions (e.g. cause and effect) and transitive correlations, as well as removing noise due to insufficient number of samples relative to the number of variables. In this talk, I will discuss an Ornstein-Zernike-like approach for data analysis that disentangles correlations in datasets using ideas from the theory of liquids. The Ornstein-Zernike closure is parameterised by deep learning, and a framework inspired by random matrix theory is used to remove finite sampling noise. I will illustrate this approach by applying it to problems such as ligand-based virtual screening and predicting protein function from sequence covariation.

This talk is part of the Theory - Chemistry Research Interest Group series.

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