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 highthroughput 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.
Keywords Asymmetric catalysis · Chiral ligand design · Computational modelling ·
Enantiomeric excess · Quantitative structure-selectivity relationships (QSSR)
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
0 -Bi-2-naphthol
BINAP
2,2
0 -Bis(diphenylphosphino)-1,1
0 -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) 2 PHAL 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
154
R. Ardkhean et al.
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 highthroughput 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.
Keywords Asymmetric catalysis · Chiral ligand design · Computational modelling ·
Enantiomeric excess · Quantitative structure-selectivity relationships (QSSR)
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
0 -Bi-2-naphthol
BINAP
2,2
0 -Bis(diphenylphosphino)-1,1
0 -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) 2 PHAL 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
154
R. Ardkhean et al.