Dr Emma King-Smith
Contact details
- Email: emma.king-smith@ed.ac.uk
- Web: King-Smith Group Website
Address
- Street
-
Rm 241
University of Edinburgh
Joseph Black Building
David Brewster Road - City
- Edinburgh
- Post code
- EH9 3FJ
Research summary
synthetic organic chemistry, reaction prediction, machine learning, artificial intelligence
Current research interests
Synthetic organic chemistry has many open challenges, particularly around intrinsic molecular reactivity. For example, how reaction yields change as reactants and conditions change or how molecules interact with the human body. Emma's research harnesses advances in deep learning and AI, with an emphasis on experimental validation, to build practical and robust predictive models that deepen our understanding about inherent reactivity of small organic compounds.William McCorkindale, Mihajlo Filep, Nir London, Alpha A. Lee, and Emma King-Smith*, "Deconvoluting Low Yield from Weak Potency in Direct-to-Biology Workflows with Machine Learning", RSC Med. Chem. 2024, 15, 1015–1021.
Emma King-Smith*, "Transfer Learning for a Foundational Chemistry Model", Chem. Sci. 2024, 15, 5143–5151.
Emma King-Smith, Felix A. Faber, Usa Reilly, Anton V. Sinitskiy, Qingyi Yang, Bo Liu, Dennis Hyek, and Alpha A. Lee*, "Predictive Minisci Late Stage Functionalization with Transfer Learning", Nature Commun. 2024, 15, 426–438.
Emma King-Smith, Simon Berritt, Louise Bernier, Xinjun Hou, Jacquelyn L. Klug-McLeod, Jason Mustakis, Neal W. Sach, Joseph Tucker, Qingyi Yang, Roger M. Howard, Alpha A. Lee*, " Probing the Chemical 'Reactome' with High Throughput Experimentation", Nature Chem. 2024, 16, 633–643.
For a full list of publications please see Emma's Google Scholar: https://scholar.google.co.uk/citations?user=0XSdP3EAAAAJ&hl=en