Office: Chemistry Research 313
Phone: (970) 791-7746
Google Scholar: https://scholar.google.co.uk/citations?hl=en&user=V6SXhCsAAAAJ
- PhD, University of Cambridge
The Paton group uses computational and data-driven approaches to make synthetic chemistry more predictable. We develop tools to predict molecular properties, design new functional molecules and optimize synthetic routes. Our approach combines quantum mechanical calculations, physical organic chemistry and machine learning.
Computer-Aided Catalyst Design
The discovery of new catalysts drives chemistry forwards, yet this is still dependent on trial-and-error experimentation. Screening large number of molecules, additives and solvent systems is innefficient, costly and wasteful. We explore computational approaches to understand and explore structure, mechanism and selectivity in catalytic transformations. Increasingly, this is carried out predictively, rather than retrospectively, in the design and optimization of new chiral catalysts to achieve high levels of stereocontrol. Collaborations with leading research groups in catalysis and synthetic organic chemistry have been established to pursue these goals.
Like most scientists, chemists are drowning in data from laboratory experiments and from calculations. We are developing tools to automate the analysis of quantum-chemistry. Another area in need of automation is in the development of quantitative structure-property relationships, particularly where flexible molecules are concerned.
- Importance of Engineered and Learned Molecular Representations in Predicting Organic Reactivity, Selectivity, and Chemical Properties Accounts of Chemical Research, 2021,
- GoodVibes: automated thermochemistry for heterogeneous computational chemistry data F1000Research, 2020, 9, 291,
- Selective Halogenation Using Designed Phosphine Reagents Journal of the American Chemical Society, 2020, 142, 11295–11305,
- Prediction of homolytic bond dissociation enthalpies for organic molecules at near chemical accuracy with sub-second computational cost Nature Communications, 2020, 11, 2328,
- Cofactor-independent pinacolase directs non-Diels-Alderase biogenesis of the Brevianamides Nature Catalysis, 2020, 3, 497-506,
- An Alkyne Linchpin Strategy for Drug: Pharmacophore Conjugation: Experimental and Computational Realization of a meta-Selective Inverse Sonogashira Coupling Journal of the American Chemical Society, 2020, 142, 3762–3774,
- Fungal Indole Alkaloid Biogenesis Through Evolution of a Bifunctional Reductase/Diels-Alderase Nature Chemistry, 2020, 11, 972–980, 2019
- Heterobiaryl synthesis by contractive C–C coupling via P(V) intermediates Science, 2018, 62, 799-804.,
- Asymmetric Nucleophilic Fluorination under Hydrogen Bonding Phase Transfer Catalysis Science, 2018, 360, 638–642,
- Cation–pi interactions in protein-ligand binding: theory and data-mining reveal different roles for lysine and arginine Chemical Science, 2018, 9, 2655–2665.,