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Accelerating chemical synthesis through machine learning and rational experiment design


Supervisor: Dr Alpha Lee with Professor Matthew Gaunt, Department of Chemistry

Making a complex chemical molecule from simpler constituents is the key challenge in organic chemistry. The state of the art is often trial-and-error. However, vast amounts of reactions are reported in the chemistry literature, and recent technological advances have made high throughput synthesis experiments and high throughput quantum chemistry calculations possible. The key question is how to assimilate those disparate pieces of data to realize a platform that can suggest synthetic pathways given a target molecule.


The first stage of the project will focus on predicting the outcome of chemical reactions using high throughput experiments as training data. Guided by quantum chemistry, we will construct predictive molecular descriptors. The second stage of the project will focus on rational experiment design - developing models that can suggest new experiments that could lead to the greatest information gain. Once diverse classes of reactions have been modeled, the ultimate goal of the project is to realize a computational synthesis recommender that recommends feasible synthetic schemes.