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 , and optimal design of experiments by combining coarse and fine measurements .
 A. A. Lee, M. P. Brenner and L. J. Colwell, Proc. Natl. Acad. Sci. U.S.A., 113, 13564 (2016)
 A. A. Lee, M. P. Brenner and L. J. Colwell, arXiv:1702.06001