Methods for predicting reaction selectivity

1. Methods for predicting reaction selectivity Per-Ola Norrby BigChem Training School 9 May 2019

2. Chemical reactionselectivity, forward prediction Reduction Chemoselectivity Regioselectivity Enantioselectivity

3. Chemical reactionselectivity, cont. Late StageFunctionalization C-H activation ElectrophilicAromaticbromination Radicalbromination Pd-catalyzed C-H activation

4. G‡ Calculating reaction selectivity Boltzmann summation 1-10 paths 1000’s possible!

5. Modeling catalyst stereoselectivity (I): QSAR QSSR: QuantitativeStructureSelectivity Relationship Norrby, Lipkowits, Kozlowski, Bo, Harvey, Fey, ..., Sigman, Doyle, …

6. Modeling catalyst stereoselectivity (II): QM (R)- vs. (S)-energy, DFT-based screening Maeda, Morokuma, Zimmerman, ..., Wheeler 27% ee → 97% ee MUE 4 kJ/mol

7. Modeling catalyst stereoselectivity (III): Q2MM MolecularMechanics @ TS, conformationalsearch

8. Molecular Mechanics for Reactivity Westheimer 1946 Houk Transitionstate force fields, TSFF De Tar, Paterson, … 1950 1960 1970 1980 1990 Sprecher 1965 Menger 1989 ”Fudgit” Using 200 parameters to fit 20 data points is not a goodidea Can the overfitting problem be solved?

9. Parameterization from QM data • QMcalculationsgive abundant data • Determining force fields from quantum mechanical data: • Hagler, Kollman, Goddard, … • Bond lengths, angles from optimizedstructures • Charges from electrostatic potentials • Torsions from calculated rotation profiles • Force constants from calculated vibrations (Hessian data) • Problem: wrongcurvature

10. Reaction coordinate modification Diagonalization: Eigenvalue replacement: Forming the new Hessian:

11. Q2MM results MUE = 2.8 kJ/mol

12. Virtual screening development and workflow

13. Virtual screening ofcatalysts: CatVS • Buildstructures • Conformationalsearch • Calculatee.r.

14. Rh-catalyzedenamidehydrogenation Collaboration with Paul Helquist and Olaf Wiest, Notre Dame, IN Q2MM force field for TS Olaf Wiest Paul Helquist

15. Asymmetrichydrogenation – Test I ΔΔG* = 10 kJ/mol e.r. = 50  98:2 e.e. = 96% Underlying program changed - Energies no longer the same Current version: no model solvent Developed for: Tested on: Enrichment, 4 of 5!

16. Asymmetrichydrogenation – Test 2 ΔΔG* = 10 kJ/mol e.r. = 50  98:2 e.e. = 96% Developed for: Tested on: • 1 good, 1 fair, 1 false negative • Acceptable

17. CatVS - Summary Combinewithligandslibrary Draw substrate & Click ”submit” Email results for all ligands Determine all lowenergy TS • Needs Q2MM force fields; significant development time • Development tools on www.github.com/Q2MM/ • Significant enrichment • Makes experimental testing more efficient • Faster routes to chiral compounds Chemical selectivity can now be reliably predicted!

18. Ongoing Christian Sköld Maria Vergou • Asymmetric Suzuki-Miyaura Liam Byrne Peter Smith Rachel Munday

19. Hybrid ML-mechanistic model Machinelearning Mechanisticmodel Hybrid model Electrophilicaromatic substitution: Tomberg, Johansson, Norrby, J. Org. Chem. 2019,4695

20. Acknowledgment AstraZeneca Tobias Rein Per Ryberg Simone Tomasi Elaine Limé David Buttar Rachel Munday Maria Vergou Peter Smith Liam Byrne Anna Tomberg Notre Dame Paul Helquist Olaf Wiest Patrick Donoghue Elsa Kieken Vincenzo Verdolino Aaron Forbes Eric Hansen Brandon Tutkowski Anthony Rosales Taylor Quinn Jessica Wahlers Xin Zhang (Shenzhen) Uppsala Christian Sköld Denmark Tommy Liljefors Torben Rasmussen Peter Brandt Peter Fristrup Lund Ulf Ryde Patrik Rydberg Stockholm Björn Åkermark $$$ AstraZeneca, The University of Gothenburg, COST, FP7/SYNFLOW, The Swedish Research Council, C3SE/Gothenburg