Connecting the complexity of stereoselective synthesis to the evolution of predictive tools

Abstract

Synthetic methods have seemingly progressed to an extent where there is an apparent and increasing need for predictive models to navigate the vast chemical space. Methods for anticipating and optimizing reaction outcomes have evolved from simple qualitative pictures generated from chemical intuition to complex models constructed from quantitative methods like quantum chemistry and machine learning. These toolsets are rooted in physical organic chemistry where fundamental principles of chemical reactivity and molecular interactions guide their development and application. Here, we detail how the evolution of these methods is a successful outcome and a powerful response to the diverse synthetic challenges confronted and the innovative selectivity concepts introduced. In this review, we perform a periodization of organic chemistry focusing on strategies that have been applied to guide the synthesis of chiral organic molecules.

Fig. 1. Illustrative model of (A) Cram's rule; (B) Felkin–Anh model; (C) Cram's chelating model (R_L = large substituent, R_M = medium substituent, R_S = small substituent, depending on steric size; Nu = nucleophile, M = external Lewis acid).

Fig. 2. A) Zimmerman–Traxler model (determination of relative configuration); (B) an illustrative example for an Evans aldol reaction (absolute configuration controlled by the chiral auxiliary).

Fig. 3. (A) 3D structure of (−)-sparteine; (B) sparteine-mediated asymmetric lithiation and results with other diamines as comparisons.

Fig. 4. The quadrant diagram for Ru/DIPAMP catalyst in asymmetric hydrogenation and the design of a non-symmetric ligand based on quadrant diagram.

Fig. 5. Illustration of the relationship between ΔΔG^‡ and enantioselectivity (e.r. and ee). [R] and [S] represent the concentration of R- and S-product after the reaction, respectively.

Fig. 6. Selected results of calculated and experimental results on hydride addition to (A) acyclic ketones and (B) cyclic ketones. Results generated by the MM2 force field.

Fig. 7. (A) Proline-catalyzed intermolecular aldol reactions. (B) Possible TSs leading to four diastereoisomers for a proline-catalyzed reaction of cyclohexanone and isobutyraldehyde. Calculated at B3LYP/6-31G* level and under 298 K.

Fig. 8. Proline-catalyzed syn-Mannich reaction and the computational design of a new catalyst for anti-Mannich reaction. (A) Proline-catalyzed syn-Mannich reaction, TS for major product (calculated at B3LYP/6-31G* level) and designed catalyst TS for anti-Mannich reaction (calculated at HF/6-31G* level); (B) experimental validation. PMP = p-methoxyphenyl.

Fig. 9. Investigation of different activation modes on a CPA-catalyzed transfer hydrogenation with a truncated phosphoric acid catalyst. Results calculated at PCM(toluene)-B3LYP/6-311++G**//B3LYP/6-31+G* level.

Fig. 10. (A) Two alternative views of the BINOL–CPA catalysts. (B) Goodman's model on mechanisms of CPA-catalyzed imine additions.

Fig. 11. Incorporation of dispersion correction provides new mechanistic insights on organocatalyzed reactions: (A) possible conformers of iminium intermediates of MacMillan imidazolidinone catalysis (energy calculated at various theoretical methods); (B) attractive dispersion interactions instead of steric repulsion determine the enantioselectivity of CBS reductions (previously-studied TS calculated at B3LYP/6-31+G(d,p) in ref. ; proposed alternative TS calculated at B3LYP-D3(BJ)/6-311+G(d,p)-SMD(THF)//B3LYP-D3(BJ)/6-311G(d,p) in ref. 79).

Fig. 12. Early examples on building linear correlations between enantioselectivity and reaction parameters.

Fig. 13. Exploring the interdependence of substrate and catalyst effects in NHK-type propargylation reactions through Sterimol parameters B₁ and B₅. The graphical representation shows Sterimol values using an isopropyl group as an example, oriented along the primary bond axis. The two width measurements, B₁ (minimum width) and B₅ (maximum width), are taken perpendicular to this axis. Adapted with permission from ref. . Copyright 2013, American Chemical Society.

Fig. 14. Comprehensive MLR models for CPA-catalyzed nucleophilic addition to imines.

Fig. 15. (A) Average steric occupation (ASO) for description of CPA catalyst. Adapted with permission from ref. . Copyright 2019, The American Association for the Advancement of Science. (B) Multiple fingerprint features as molecular descriptors. (C) Fragment descriptors for IDPi catalysts and condensed graph of reactions (CGR) for intramolecular reactions.

Fig. 16. (A) Features and descriptors derived from chemical space networks (CSN) enable better predictions for IDPi-catalyzed reactions. (B) “Key intermediate” graph as a new representation of reaction intermediates for graphical neural networks (GNN).

Jiajing Li

Jolene P. Reid