Emma King-Smith

University of Cambridge, UK



Emma began her career as an experimental chemist, performing undergraduate research with Prof. Dean Toste (University of California, Berkeley) in organometallics methods development. Upon graduation, she joined the process chemistry team at Genentech (South San Francisco) for a year and gained a deep appreciation for total synthesis, prompting her return to academia for a PhD. Emma's graduate studies were completed under the tutelage of Prof. Hans Renata (The Scripps Research Institute Jupiter, Florida). During her tenure there, she gained expertise in enzyme-mediated total synthesis, developing novel routes to a wide range of molecules. Emma continued her journey as a synthetic chemist as a Newton International Fellow (Royal Society) in the groups of Dr. Alpha Lee and Prof. Matthew Gaunt (University of Cambridge), with an emphasis in machine learning and applied to synthetic chemistry.


Practical Machine Learning for Organic Small Molecule Modelling


Synthetic chemistry has many open challenges: how reaction yields change as reactants and conditions change, how molecules interact with the human body, or the full underlying mechanisms of some workhorse reactions. Machine learning (ML) has seen enormous strides in modelling the world's "black boxes": from image processing and recognition that rival human ability, consistently beating human players in a variety of games, to the amusing ruminations of the latest large language models. Due to the low standardization of data, few large chemistry-focused datasets, and the mere fact that molecules are difficult systems to model, ML has historically struggled to make headway in the chemical sciences. Recent developments in ML models and increased access to open-source chemistry datasets have opened the door to practical ML models, including DFT and molecular property predictions and biological activity predictions. Herein, we present case studies utilizing recent and classic ML methods to further our predictive ability and understanding of synthetic chemistry.
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