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Exploring Chemical Space with Random Matrix Theory

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Developing computational methods to explore chemical space is a major challenge for drug discovery and material discovery. The challenge is often the limited number of experimental measurements relative to the vast chemical space. I will discuss a mathematical framework, inspired by random matrix theory, which allows us to remove noise due to finite sampling and identify important chemical features. I will illustrate this framework with three examples: predicting protein-ligand affinity, optimal design of experiments by combining coarse and fine measurements, and inferring a probabilistic generative model by combining liquid state theory with deep learning.

This talk is part of the Theory of Condensed Matter series.

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