Holistic prediction of enantioselectivity in asymmetric catalysis


When faced with unfamiliar reaction space, synthetic chemists typically apply the reported conditions (reagents, catalyst, solvent and additives) of a successful reaction to a desired, closely related reaction using a new substrate type. Unfortunately, this approach often fails owing to subtle differences in reaction requirements. Consequently, an important goal in synthetic chemistry is the ability to transfer chemical observations quantitatively from one reaction to another. Here we present a holistic, data-driven workflow for deriving statistical models of one set of reactions that can be used to predict out-of-sample reactions. As a validating case study, we combined published enantioselectivity datasets that employ 1,1′-bi-2-naphthol (BINOL)-derived chiral phosphoric acids for a range of nucleophilic addition reactions to imines and developed statistical models. These models reveal the general interactions that impart asymmetric induction and allow the quantitative transfer of this information to new reaction components. This technique creates opportunities for translating comprehensive reaction analysis to diverse chemical space, streamlining both catalyst and reaction development.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Fig. 1: Workflow for interrogating and applying mechanistic transferability.
Fig. 2: Comprehensive model development.
Fig. 3: Development of focused correlations.
Fig. 4: Out-of-sample predictions using two-tiered prediction workflow.

Data availability

All data relating to this study is available in the Supplementary Information.

Code availability

All code used for model development is available in the Supplementary Information.


  1. 1.

    Houk, K. N. & Cheong, P. H.-Y. Computational prediction of small-molecule catalysts. Nature 455, 309–313 (2008).

  2. 2.

    Davis, H. J. & Phipps, R. J. Harnessing non-covalent interactions to exert control over regioselectivity and site-selectivity in catalytic reactions. Chem. Sci. 8, 864–877 (2017).

  3. 3.

    Knowles, R. R. & Jacobsen, E. N. Attractive noncovalent interactions in asymmetric catalysis: links between enzymes and small molecule catalysts. Proc. Natl Acad. Sci. USA 107, 20678–20685 (2010).

  4. 4.

    Sigman, M. S., Harper, K. C., Bess, E. N. & Milo, A. The development of multidimensional analysis tools for asymmetric catalysis and beyond. Acc. Chem. Res. 49, 1292–1301 (2016).

  5. 5.

    Ahneman, D. T., Estrada, J. G., Lin, S., Dreher, S. D. & Doyle, A. G. Predicting reaction performance in C-N cross-coupling using machine learning. Science 360, 186–190 (2018).

  6. 6.

    Chuang, K. V. & Keiser, M. J. Comment on “Predicting reaction performance in C-N cross-coupling using machine learning”. Science 362, eaat8603 (2018).

  7. 7.

    Estrada, J. G., Ahneman, D. T., Sheridan, R. P., Dreher, S. D. & Doyle, A. G. Response to ‘Comment on “Predicting reaction performance in C-N cross-coupling using machine learning”’. Science 362, eaat8763 (2018).

  8. 8.

    Robbins, D. W. & Hartwig, J. F. A simple, multidimensional approach to high-throughput discovery of catalytic reactions. Science 333, 1423–1427 (2011).

  9. 9.

    McNally, A., Prier, C. K. & MacMillan, D. W. C. Discovery of an alpha-C-H arylation reaction using the strategy of accelerated serendipity. Science 334, 1114–1117 (2011).

  10. 10.

    Neel, A. J., Milo, A., Sigman, M. S. & Toste, F. D. Enantiodivergent fluorination of allylic alcohols: dataset design reveals structural interplay between achiral directing group and chiral anion. J. Am. Chem. Soc. 138, 3863–3875 (2016).

  11. 11.

    Walsh, P. J. & Kozlowski, M. C. Fundamentals of Asymmetric Catalysis (University Science Books, 2008).

  12. 12.

    Yoon, T. P. & Jacobsen, E. N. Privileged chiral catalysts. Science 299, 1691–1693 (2003).

  13. 13.

    Yamamoto, H. Lewis Acids in Organic Synthesis (Wiley, 2000).

  14. 14.

    Akiyama, T. Stronger Brønsted acids. Chem. Rev. 107, 5744–5758 (2007).

  15. 15.

    Collins, K. D. & Glorius, F. Intermolecular reaction screening as a tool for reaction evaluation. Acc. Chem. Res. 48, 619–627 (2015).

  16. 16.

    Gesmundo, N. J. et al. Nanoscale synthesis and affinity ranking. Nature 557, 228–232 (2018).

  17. 17.

    Reetz, M. T. Laboratory evolution of stereoselective enzymes: a prolific source of catalysts for asymmetric reactions. Angew. Chem. Int. Ed. 50, 138–174 (2011).

  18. 18.

    Hansen, E., Rosales, A. R., Tutkowski, B., Norrby, P.-O. & Wiest, O. Prediction of stereochemistry using Q2MM. Acc. Chem. Res. 49, 996–1005 (2016).

  19. 19.

    Metsänen, T. T. et al. Combining traditional 2D and modern physical organic-derived descriptors to predict enhanced enantioselectivity for the key aza-Michael conjugate addition in the synthesis of PrevymisTM (letermovir). Chem. Sci. 9, 6922–6927 (2018).

  20. 20.

    Robak, M. T., Herbage, M. A. & Ellman, J. A. Synthesis and applications of tert-butanesulfinamide. Chem. Rev. 110, 3600–3740 (2010).

  21. 21.

    Kobayashi, S., Mori, Y., Fossey, J. S. & Salter, M. M. Catalytic enantioselective formation of C–C bonds by addition to imines and hydrazones: a ten-year update. Chem. Rev. 111, 2626–2704 (2011).

  22. 22.

    Nugent, T. C. Chiral Amine Synthesis: Methods, Developments and Applications (Wiley, 2010).

  23. 23.

    Silverio, D. L. et al. Simple organic molecules as catalysts for enantioselective synthesis of amines and alcohols. Nature 494, 216–221 (2013).

  24. 24.

    Parmar, D., Sugiono, E., Raja, S. & Rueping, M. Complete field guide to asymmetric BINOL-phosphate derived Brønsted acid and metal catalysis: history and classification by mode of activation; Brønsted acidity, hydrogen bonding, ion pairing, and metal phosphates. Chem. Rev. 114, 9047–9153 (2014).

  25. 25.

    Simón, L. & Goodman, J. M. Theoretical study of the mechanism of Hantzsch ester hydrogenation of imines catalyzed by chiral BINOL-phosphoric acids. J. Am. Chem. Soc. 130, 8741–8747 (2008).

  26. 26.

    Reid, J. P., Simón, L. & Goodman, J. M. A practical guide for predicting the stereochemistry of bifunctional phosphoric acid catalyzed reactions of imines. Acc. Chem. Res. 49, 1029 (2016).

  27. 27.

    Santiago, C. B., Guo, J.-Y. & Sigman, M. S. Predictive and mechanistic multivariate linear regression models for reaction development. Chem. Sci. 9, 2398–2412 (2018).

  28. 28.

    Reid, J. P. & Sigman, M. S. Comparing quantitative prediction methods for the discovery of small-molecule chiral catalysts. Nat. Rev. Chem. 2, 290–305 (2018).

  29. 29.

    Denmark, S. E., Gould, N. D. & Wolf, L. M. A systematic investigation of quaternary ammonium ions as asymmetric phase-transfer catalysts. Application of quantitative structure activity/selectivity relationships. J. Org. Chem. 76, 4337–4357 (2011).

  30. 30.

    Hansch, C. & Leo, A. Exploring QSAR: Fundamentals and Applications in Chemistry and Biology (ACS, 1995).

  31. 31.

    Reid, J. P. & Goodman, J. M. Goldilocks catalysts: computational insights into the role of the 3,3′ substituents on the selectivity of BINOL-derived phosphoric acid catalysts. J. Am. Chem. Soc. 138, 7910–7917 (2016).

  32. 32.

    Terada, M., Machioka, K. & Sorimachi, K. High substrate/catalyst organocatalysis by a chiral Brønsted acid for an enantioselective aza-ene-type reaction. Angew. Chem. Int. Ed. 45, 2254–2257 (2006).

  33. 33.

    Chen, M.-W. et al. Organocatalytic asymmetric reduction of fluorinated alkynyl ketimines. J. Org. Chem. 83, 8688–8694 (2018).

  34. 34.

    Zahrt, A. F. et al. Prediction of higher-selectivity catalysts by computer-driven workflow and machine learning. Science 363, eaau5631 (2019).

Download references


J.P.R. thanks the EU Horizon 2020 Marie Skłodowska-Curie Fellowship (grant 792144) and M.S.S. thanks the NIH (grant GM-121383) for support of this work. Computational resources were provided from the Center for High Performance Computing (CHPC) at the University of Utah and the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by the NSF (grant ACI-1548562) and provided through allocation TG-CHE180003.

Author information

J.P.R. designed and performed all computations and statistical analyses. Both authors contributed to the analysis and writing of the manuscript.

Correspondence to Matthew S. Sigman.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data figures and tables

Extended Data Fig. 1 Reaction component comparison.

Parameterization challenges for the identification of numerical descriptors in reaction dimension, demonstrated using two reactions that represent the extremes of multidimensional feature space. MS, molecular sieves.

Supplementary information

Supplementary Information

This file contains a full list of authors in the Gaussian 09 reference; Computational Methods; Cartesian Coordinates of all the Substrate, Catalyst and Solvent Structures; Collected Parameters; Data Curation; Model Development and Supplementary References.

Supplementary Table 1

This file contains the parameter tables.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark


By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.