Robert Paton Professor

Office: Chemistry Research 313

Phone: (970) 791-7746

Website: http://patonlab.com

Curriculum Vitae: http://patonlab.com/PATON_CV_WEB.pdf

Google Scholar: https://scholar.google.co.uk/citations?hl=en&user=V6SXhCsAAAAJ

Education

  • PhD, University of Cambridge

About

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.

Data-driven Chemistry

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.

 

Publications

Importance of Engineered and Learned Molecular Representations in Predicting Organic Reactivity, Selectivity, and Chemical Properties Gallegos, L. C.; Luchini, G.; St. John, P. C.; Kim, S.; Paton, R. S. Accounts of Chemical Research, 2021,
GoodVibes: automated thermochemistry for heterogeneous computational chemistry data Luchini, G.; Alegre-Requena, J. V.; Funes-Ardoiz, I.; Paton, R. S. F1000Research, 2020, 9, 291,
Selective Halogenation Using Designed Phosphine Reagents Levy, J. N.; Alegre-Requena, J. V.; Liu, R.; Paton, R. S.; McNally, AJournal 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 St John, P. C.; Guan, Y.; Kim, Y.; Kim, S.; Paton, R. S.Nature Communications, 2020, 11, 2328,
Cofactor-independent pinacolase directs non-Diels-Alderase biogenesis of the Brevianamides Ye, Y.; Du, L.; Zhang, X.; Newmister, S. A.; McCauley, M.; Alegre-Requena, J. V.; Zhang W.; Mu, S.; Minami, A.; Fraley, A. E.; Adrover-Castellano, M. L.; Carney, N.; Shende, V. K.; Oikawa, H.; Kato H.; Tsukamoto, S.; Paton, R. S.; Williams R. M.; , Sherman, D. H.; Li, S.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 Porey, S.; Zhang, X.; Bhowmick, S.; Singh, V. K.; Guin, S.; Paton, R. S.; Maiti, D.Journal of the American Chemical Society, 2020, 142, 3762–3774,
Fungal Indole Alkaloid Biogenesis Through Evolution of a Bifunctional Reductase/Diels-Alderase Dan, Q.; Newmister, S. A.; Klas, K. R.; Fraley, A. E.; McAfoos, T. J.; Somoza, A. D.; Sunderhaus, J. D.; Ye, Y.; Shende, V. V.; Yu, F.; Sanders, J. N.; Brown, W. C.; Zhao, L.; Paton, R. S.; Houk, K. N.; Smith, J. L.; Sherman, D. H.; Williams, R. M. Nature Chemistry, 2020, 11, 972–980, 2019
Heterobiaryl synthesis by contractive C–C coupling via P(V) intermediates Hilton, M. C.; Zhang, X.; Boyle, B. T.; Alegre-Requena, J. V.; Paton, R. S.; McNally, A.Science, 2018, 62, 799-804.,
Asymmetric Nucleophilic Fluorination under Hydrogen Bonding Phase Transfer Catalysis Pupo, G.; Ibba, F.; Ascough, D. M. H.; Vicini, A. C.; Ricci, P.; Christensen, K.; Morphy, J. R.; Brown, J. M.; Paton, R. S.; Gouverneur, V. Science, 2018, 360, 638–642,
Cation–pi interactions in protein-ligand binding: theory and data-mining reveal different roles for lysine and arginine Kumar, K.; Woo, S. M.; Siu, T.; Cortopassi, W. A.; Duarte, F.; Paton, R. S.Chemical Science, 2018, 9, 2655–2665.,