Speaker
Description
Abstract: Machine learning models are very successful in predicting various chemical properties. Graph-convolutional neural networks (GCNNs) are routinely used for the prediction of molecular properties, but their application to chemical reactions is largely unexplored. GCNNs allow for a learned extraction of important characteristics of a molecule and enable end-to-end learning, instead of relying on expert, system-dependent knowledge. However, the properties of chemical reactions, i.e. the combination of reactant and product molecules, are not readily accessible with current GCNNs which are designed to take molecular graphs as input. Recently, GCNNs based on the condensed graph of reaction (CGR) were shown to unlock the full potential of GCNNs also for reactions, where reactants and products are merged into a single pseudo-molecular graph, i.e. an artificial graph transition state. In this workshop, the anatomy of molecular GCNNs will be discussed in detail, as well as the changes necessary to encode reactions instead of molecules, including hands-on exercises to build your own reaction GCNN. Compared to previous approaches, GCNNs on CGRs offer a comparable or better performance with a lower number of parameters. We showcase the performance on different tasks, such as the prediction of barrier heights or rate constants, as well as the chemo- and regioselectivity of reactions.
Bio: Esther Heid obtained her Bachelor’s (2014), Master’s (2016) and PhD (2019) degree in Chemistry from the University of Vienna, Austria. Her thesis focused on the molecular dynamics simulation of soft matter, as well as quantum mechanical calculations for obtaining force field parameters. In 2020 she joined the Massachusetts Institute of Technology, holding an Erwin-Schroedinger Postdoctoral Fellowship from the Austrian Science Fund, which enables her to conduct research on the development of computer-aided tools for finding novel multi-enzyme networks which yield a specified target molecule. The project utilizes recent developments in machine learning, bioretrosynthesis, and cheminformatics, and aims toward a more efficient, selective and environmentally favorable synthesis of compounds through the inclusion of biocatalytic transformations. A major part of the project is concerned with developing new machine learning methods for molecular and reaction property predictions. Her postdoc fellowship includes a one-year return-phase in Austria to finish up the project, which she started in 2022 at the Vienna University of Technology.
