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Ligand Design for Asymmetric Catalysis: Combining Mechanistic and Chemoinformatics Approaches

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New Directions in the Modeling of Organometallic Reactions

Part of the book series: Topics in Organometallic Chemistry ((TOPORGAN,volume 67))

Abstract

A core element to the successful development of asymmetric catalytic reactions is finding a suitable chiral catalyst or ligand. The discovery and optimization of chiral catalysts can be enormously challenging. Traditionally, chemists have approached this endeavour by screening existing ligands. The most promising structures are then modified based on mechanistic knowledge, chemical intuition and the results of screening experiments, with the aim of optimizing selectivity and yield. However, this empirical approach has begun to change: new methods to accelerate the experimental screening process have emerged together with computational and physical-organic approaches that provide a systematic, and hopefully faster, route to new catalysts. Practical and theoretical understanding of high-throughput screening and multi-parameter optimization are now requirements at the cutting edge of the field, in addition to synthetic and mechanistic expertise.

In this chapter, we summarize the recent examples of combinatorial approaches taken to discover and develop asymmetric catalytic transformations. In particular, we highlight the use of quantitative models to predict reaction outcomes. A series of guidelines are presented to aid chemists in adopting these approaches, followed by illustrated examples of recent work in this area.

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Abbreviations

AARON:

An automated reaction optimizer for new (catalysts)

AD:

Applicability domain

AIC:

Akaike information criterion

ANOVA:

Analysis of variance

ASO:

Average steric occupancy

BINOL:

1,1′-Bi-2-naphthol

BINAP:

2,2′-Bis(diphenylphosphino)-1,1′-binaphthyl

CAPT:

Chiral anion phase transfer

cat.:

Catalytic

CIP:

Cahn-Ingold-Prelog

COD:

1,5-Cyclooctadiene

dba:

Dibenzylideneacetone

DCM:

Dichloromethane

DFT:

Density functional theory

(DHQD)2PHAL:

Hydroquinidine 1,4-phthalazinediyl diether

DNA:

Deoxyribonucleic acid

dr:

Diastereomeric ratio

ee:

Enantiomeric excess

EPR:

Electron paramagnetic resonance

er:

Enantiomeric ratio

etc.:

et cetera

FF:

Force field

GC-MS:

Gas chromatography-mass spectrometry

HPLC:

High-performance liquid chromatography

hr:

Hour(s)

HRMS:

High-resolution mass spectrometry

HTS:

High-throughput screening

IR:

Infrared

kcal:

Kilocalories

kJ:

Kilojoules

LMOCV:

Leave-many-out cross-validation

LOOCV:

Leave-one-out cross-validation

m :

Molarity

Min:

Minute(s)

MLR:

Multivariate linear regression

MM:

Molecular mechanics

mol:

Mole

NBO:

Natural bond order

NBS:

N-Bromosuccinimide

NMR:

Nuclear magnetic resonance

OECD:

Organization for Economic Co-operation and Development

OLS:

Ordinary least squares regression

PA:

Phosphate anion

PCA:

Principal component analysis

PLS:

Partial least squares regression

Q2MM:

Quantum guided molecular mechanic

QM:

Quantum mechanic

QSAR:

Quantitative structure-activity relationship

QSSR:

Quantitative structure-selectivity relationship

RMSE:

Root-mean-square error

rt:

Room temperature

SD:

Standard deviation

TADDOL:

α,α,α′,α′-Tetraaryl-2,2-disubstituted 1,3-dioxolane-4,5-dimethanol

TMS:

Trimethyl silyl

TS:

Transition states

UTS:

Universal training set

UV-vis:

Ultraviolet-visible spectroscopy

vs:

Versus

XPS:

X-ray photoelectron spectroscopy

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Correspondence to Robert S. Paton .

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Ardkhean, R., Fletcher, S.P., Paton, R.S. (2020). Ligand Design for Asymmetric Catalysis: Combining Mechanistic and Chemoinformatics Approaches. In: Lledós, A., Ujaque, G. (eds) New Directions in the Modeling of Organometallic Reactions. Topics in Organometallic Chemistry, vol 67. Springer, Cham. https://doi.org/10.1007/3418_2020_47

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