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Ermanis Research Group

Computational Exploration of Organic Reactions

We are a recently established, growing research group at the School of Chemistry, University of Nottingham. Our aim is to explore, design and discover new, synthetically useful organic reactions through synergistic use of computations, machine learning and collaborative experiments.

We have both funded and self-funded PhD positions available, starting October 2022 or potentially sooner. Please get in touch with Dr Ermanis (kristaps.ermanis@nottingham.ac.uk) for more details.

Latest news:

11/10/2021 - Warm welcome to our new undergraduate students Rachel Heelas, Rohan Patel and Metin Tamer, who have joined the group for their final year projects.

01/07/2021 - Dr Ermanis joins the School of Chemistry at University of Nottingham to start his independent career.

Dr Kristaps Ermanis
Assistant Professor of Chemistry
School of Chemistry
University of Nottingham
University Park
Nottingham
NG7 2RD
United Kingdom
Email: kristaps.ermanis@nottingham.ac.uk
Tel: +44 (0)115 95 13505

Kristaps Ermanis received his PhD in Organic Chemistry from the University of York in 2015, where he worked on the total synthesis of phorboxazole B with Prof. Paul Clarke. He then spent two years working in Prof. Jonathan Goodman group in Cambridge working on computational NMR structure elucidation. After a brief postdoc in Prof. Mike Porter group at UCL, he was awarded Leverhulme Early Career in 2017, held at Cambridge. Kristaps started his independent career as Assistant Professor at the University of Nottingham in 2021. His research interests are centred around using computational techniques to explore new organic reactions.

ORCID profile

Google Scholar profile

Publons profile

Our aim is to explore, design and discover new, synthetically useful organic reactions through synergistic use of computations, machine learning and collaborative experiments.

Computational Understanding and Design of Organic Reactions. We have previously made contributions to several challenging reaction development projects. This includes thorough mechanistic exploration of an enantioselective Minisci reaction reported by Phipps group (Science 2020, 367, 1246). During this investigation, an unexpected internal deprotonation activation mode was identified (J. Am. Chem. Soc. 2020, 142, 21091).
This mechanistic understanding has since been used in the rational design of an enantioselective alcohol Minisci reaction (submitted)

Machine Learning for Accelerated Understanding of Chemical Reactions. Chemical machine learning is a rapidly growing area, and has the potential to revolutionize the modelling of wide variety of chemical systems. We have developed a novel explainable molecular representation tailored for chemical property prediction. In combination with kernel ridge regression models and artificial neural network models this method achieves state-of-art performance in electronic and free energy predictions. Importantly, the neural network model is more data-efficient and several times faster than other models of similar accuracy. This work has been recently submitted for publication.

Computational NMR Spectra Prediction and Structure Elucidation. Our group has extensive experience in using DFT methods for NMR spectra prediction of complex organic molecules, including natural products, synthetic intermediates and drug candidates. Recently we developed a fully automated workflow DP4-AI for DFT NMR calculations, raw NMR data interpretation and statistical comparison of the two. This workflow enables effortless structure validation and elucidation, and has been recently published (Chem. Sci. 2020, 11, 4351). The code is open-source and is available on GitHub.

17) MolE8: Finding DFT Potential Energy Surface Minima Values from Force-Field Optimised Organic Molecules with New Machine Learning Representations;
S. Lee, K. Ermanis* and J. M. Goodman*
Chem. Sci. 2022, DOI: 10.1039/D1SC06324C

16) Hydrogen Atom Transfer Driven Enantioselective Minisci Reaction of Alcohols;
A. C. Colgan, R. S. J. Proctor, D. C. Gibson, P. Chuentragool, Antti S. K. Lahdenperӓ,
K. Ermanis* and R. J. Phipps*
Angew. Chem. Int. Ed. 2022, DOI: 10.1002/anie.202200266

15) Gold(I)-Catalyzed Nucleophilic Allylation of Azinium Ions with Allylboronates;
L. O'Brien, S. P. Argent, K. Ermanis* and H. W. Lam*
Angew. Chem. Int. Ed. 2022, DOI: 10.1002/anie.202202305

14) Enantioselective “Clip-Cycle” Synthesis of Di-, Tri- and Spiro-substituted Tetrahydropyrans;
K. Alomari, N. S. P. Chakravarthy, B. Duchadeau, K. Ermanis* and P. A. Clarke*
Org. Biomol. Chem. 2022, 20, 1181

13) A Computational and Experimental Investigation of the Origin of Selectivity in the Chiral Phosphoric Acid Catalyzed Enantioselective Minisci Reaction;
K. Ermanis*, A. C. Colgan, R. S. J. Proctor, B. W. Hadrys, R. J. Phipps*, and J. M. Goodman*
J. Am. Chem. Soc. 2020, 142, 21091

12) Asymmetric “Clip-Cycle” Synthesis of Pyrrolidines and Spiropyrrolidines;
C. J. Maddocks, K. Ermanis* and Paul. A. Clarke*
Org. Lett. 2020, 22, 8116

11) Synergism of Anisotropic and Computational NMR Methods Reveals the Likely Configuration of Phormidolide A;
I. E. Ndukwe, X. Wang, N. Y. S. Lam, K. Ermanis, K. L. Alexander, M. J. Bertin, G. E. Martin, G. Muir, I. Paterson, R. Britton, J. M. Goodman, E. J. N. Helfrich, J. Piel, W. H. Gerwick* and R. T. Williamson*
Chem. Commun. 2020, 56, 7565.

10) DP4-AI Automated NMR Data Analysis: Straight from Spectrometer to Structure;
A. Howarth, K. Ermanis* and J. M. Goodman*
Chem. Sci. 2020, 11, 4351

9) The Optimal DFT Approach in DP4 NMR Structure Analysis – Pushing the Limits of Relative Stereochemistry Elucidation;
K. Ermanis, K. E. B. Parkes, T. Agback and J. M. Goodman*
Org. Biomol. Chem. 2019, 17, 5886

8) BINOPtimal: A Web Tool for Optimal Chiral Phosphoric Acid Catalyst Selection;
J. P. Reid, K. Ermanis and J. M. Goodman*
Chem. Commun. 2019, 55, 1778.

7) Conversion of Alcohols to Phosphorothiolates Using a Thioiminium Salt as Coupling Agent;
H. Grounds, K. Ermanis, S. A. Newgas and M. J. Porter*
J. Org. Chem. 2017, 82, 12735.

6) Doubling the Power of DP4 for Computational Structure Elucidation;
K. Ermanis, K. E. B. Parkes, T. Agback and J. M. Goodman*
Org. Biomol. Chem. 2017, 15, 8998.

5) The stereodivergent formation of 2,6-cis and 2,6-trans-tetrahydropyrans: experimental and computational investigation of the mechanism of a thioester oxy-Michael cyclization;
K. Ermanis, Yin-Ting Hsiao, U. Kaya, A. Jeuken and P. A. Clarke*
Chem. Sci. 2017, 8, 482.

4) Expanding DP4: Application to Drug Compounds and Automation;
K. Ermanis, K. E. B. Parkes, T. Agback and J. M. Goodman*
Org. Biomol. Chem. 2016, 14, 3943.

3) Strategies for the Construction of Tetrahydropyran Rings in the Synthesis of Natural Products;
P. A. Clarke*, N. M. Nasir and K. Ermanis
Org. Biomol. Chem. 2014, 12, 3323.

2) The Development of Pot, Atom and Step Economic Syntheses of Functionalised Tetrahydropyrans, Dihydropyrans and Piperidines;
P. A. Clarke* and K. Ermanis, invited review
Curr. Org. Chem. 2013, 17, 2025.

1) Synthesis of the C20-C32 Tetrahydropyran Core of the Phorboxazoles and the C22 epimer via a Stereodivergent Michael Reaction;
P. A. Clarke* and K. Ermanis
Org. Lett. 2012, 14, 5550.

11/10/2021 - Warm welcome to our new undergraduate students Rachel Heelas, Rohan Patel and Metin Tamer, who have joined the group for their final year projects.

01/07/2021 - Dr Ermanis joins the School of Chemistry at University of Nottingham to start his independent career.

We have both funded and self-funded PhD positions available, starting October 2022 or potentially sooner. Please get in touch with Kris (kristaps.ermanis@nottingham.ac.uk) for more details.

During your PhD in our group you will become an expert in computational organic chemistry methods, and gain experience in machine learning and programming. You will have the opportunity to work on reaction development projects in collaboration with experimental researchers in Nottingham and elsewhere, with plenty of publication opportunities.
You will also work on further development of automated computational reaction exploration methods and chemical machine learning applications, providing opportunities for first-author publications.
In our group we place particular emphasis on structured training of new PhD students. We will equip you with all of the essential computational skills early on, so that you can tackle your first reaction modelling project with confidence. You will then increase the breadth and depth of this knowledge as required by your research projects, as well as steadily developing into an independent researcher.

We are always keen to support strong candidates for postdoctoral fellowship applications, including Royal Commission for the Exhibition of 1851 fellowships, Marie Sklodowska-Curie Fellowships and others.

The group takes on a number of undergraduate students for their final year research projects, and will also consider hosting summer research projects. If you are interested in conducting a research project in the group, please email Kris (kristaps.ermanis@nottingham.ac.uk).