# 12 May 2017 - Alpha Lee - Exploring chemical space by undressing finite sampling noise

/Alpha Lee from Harvard will be presenting a talk entitled "Exploring Chemical Space by Undressing Finite Sampling Noise." Whether you are interested in chemistry or just machine learning, it will certainly be an interesting discussion!

**Exploring chemical space by undressing finite sampling noise**

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 two examples: predicting protein-ligand affinity [1], and optimal design of experiments by combining coarse and fine measurements [2].

[1] A. A. Lee, M. P. Brenner and L. J. Colwell, Proc. Natl. Acad. Sci. U.S.A., 113, 13564 (2016)

[2] A. A. Lee, M. P. Brenner and L. J. Colwell, arXiv:1702.06001