The Paton Research Group

Computational Organic Chemistry

The Paton lab conducts research at the intersection of computational chemistry, organic chemistry, and machine learning. Our goal is to transform chemical discovery through computational prediction of reactivity, selectivity, and molecular properties. We develop open-source software, automated workflows, and AI-driven models to decode reaction mechanisms and guide catalyst design, working in close collaboration with experimental groups to validate predictions and accelerate the development of new synthetic transformations.

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Research

Data Driven Chemistry FI

Data-Driven Chemistry

Computer aided catalyst design

Catalyst Discovery and Design

reaction mechanism

Reaction Mechanism and Selectivity

Lab Photos

Recent Publications

Fundamental Study of Density Functional Theory Applied to Triplet State Reactivity: Introduction of the TRIP50 Dataset.

Hughes, W. B.; Popescu, M. V.; Paton, R. S.  J. Chem. Theory Comput. 2026, DOI: 10.1021/acs.jctc.6c00144

A radical–polar crossover approach to complex nitrogen heterocycles via the triplet state.

Lockhart, Z.; Popescu, M. V.; Alegre-Requena, J. V.; Ahuja, J.; Paton, R. S.; Smith, M. D. Chem 2026, 102904

Synthesis and properties of allylic, benzylic, propargylic and allenylic oxonium ions.

Chan, H. S. S.; Li, Y.; Sutro, J. L.; Brown, D. S.; Paton, R. S.; Burton, J. W. Nat. Synth. 2026, DOI: 10.1038/s44160-025-00964-8

Potassium Metabisulfite’s Role in Developing a Robust Platform for Enantioenriched N-Alkylpyridinium Salts as Piperidine Precursors.

Selingo, J. D.; King, J. R.; Pio, B.; Neel, A. J.; Lam, Y.-h.; Paton, R. S.; Maddess, M. L.; McNally, A. J. Am. Chem. Soc. 2026, 148, 8621–8633

Peptide Catalysts Conformationally Tuned for Fluoride Binding and Delivery.

Poškaitė, G.; Schlatzer, T.; Chen, Z.; Popescu, M. V.; Paton, R. S.; Gouverneur, V. J. Am. Chem. Soc. 2026, 148, 9238–9243

Deep learning for asymmetric catalysis.

Paton, R. S.; Kim, S. Nat. Comput. Sci. 2026, 6, 115–116