Berlin 2018 – scientific programme

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SYMS: Symposium Data-driven Methods in Molecular Simulations of Soft-Matter Systems

SYMS 1: Data-driven Methods in Molecular Simulations of Soft-Matter Systems

SYMS 1.4: Invited Talk

Monday, March 12, 2018, 16:45–17:15, H 0105

Liquid State Theory Meets Deep Learning and Molecular Informatics — •Alpha Lee — Department of Physics, University of Cambridge, Cambridge, United Kingdom

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.