Advances in machine learning for molecules

In this talk, I will describe two applications of machine learning to molecule data. First, I will focus on the problem of efficiently searching chemical space for new molecules with optimal properties. I will describe how to use recent advances in deep generative models to obtain continuous representations of molecules which allow us to automatically generate novel chemical structures by performing simple operations in a latent space. These methods can then be connected with Bayesian optimization techniques to accelerate the search for new molecules with optimal properties. In the second part of the talk, I will focus on the problem of modeling chemical reactions by predicting electron paths. Chemical reactions can be described as the stepwise redistribution of electrons in molecules. As such, reactions are often depicted using “arrow-pushing” diagrams which show this movement as a sequence of arrows. I will describe an electron path prediction model to learn these sequences directly from data and show that the model recovers a basic knowledge of chemistry without being explicitly trained to do so.

• Speaker: José Miguel Hernández-Lobato, Department of Engineering (University of Cambridge)
• Wednesday 20 February 2019, 14:30–15:30
• Venue: CMS, MR14.
• Series: CCIMI Seminars; organiser: Alberto J Coca.