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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5587_Библиотеки_им_академика_М_И_Перельмана.pdf
The “ETA” Indices in QSAR/QSPR/QSTR Research
Table 7. List of ETA indices corresponding to the notations used in Dragon (ver. 6) and PaDEL-Descriptor
software
Dragon (Version 6) Software Platform
Sl. No. Notation ETA Index Sl. No. Notation ETA Index
1 Eta_alpha ∑α 13 Eta_L η
2 Eta_alpha_A ∑α/N
v
14 Eta_L_A η
3 Eta_epsi ∑ε 15 Eta_F η
4 Eta_epsi_A ∑ε/N 16 Eta_F_A η′
5 Eta_betaS ∑β
6 Eta_betaS_A ∑β′
7 Eta_betaP ∑β
8 Eta_betaP_A ∑β′
s
s
ns
ns
17 Eta_FL η
18 Eta_FL_A η′
19 Eta_B η
20 Eta_B_A η′
9 Eta_beta ∑β 21 Eta_sh_p (∑α)p/∑α
10 Eta_beta_A ∑β′ 22 Eta_sh_y (∑α)
11 Eta_C η 23 Eta_sh_x (∑α)
12 Eta_C_A η/N
v
PaDEL-Descriptor Software Platform
Sl. No. Notation ETA Index Sl. No. Notation ETA Index
1 ETA_Alpha ∑α 22 ETA_Beta_s ∑β
2 ETA_AlphaP ∑α/N
3 ETA_dAlpha_A Δα
4 ETA_dAlpha_B Δα
5 ETA_Epsilon_1 ε
6 ETA_Epsilon_2 ε
7 ETA_Epsilon_3 ε
8 ETA_Epsilon_4 ε
9 ETA_Epsilon_5 ε
1
2
3
4
5
10 ETA_dEpsilon_A Δε
11 ETA_dEpsilon_B Δε
12 ETA_dEpsilon_C Δε
13 ETA_dEpsilon_D Δε
14 ETA_Psi_1 ψ
1
15 ETA_dPsi_A Δψ
16 ETA_dPsi_B Δψ
v
A
B
A
B
C
D
A
B
23 ETA_BetaP_s ∑β′
24 ETA_Beta_ns ∑β
25 ETA_BetaP_ns ∑β′
26 ETA_dBeta Δβ
27 ETA_dBetaP Δβ′
28 ETA_Beta_ns_d ∑β
29 ETA_BetaP_ns_d ∑β′
30 ETA_Eta η
31 ETA_EtaP η/N
32 ETA_Eta_R η
33 ETA_Eta_F η
34 ETA_EtaP_F η′
35 ETA_Eta_L η
36 ETA_EtaP_L η
37 ETA_Eta_R_L η
17 ETA_Shape_P (∑α)p/∑α 38 ETA_Eta_F_L η
18 ETA_Shape_Y (∑α)Y/∑α 39 ETA_EtaP_F_L η′
19 ETA_Shape_X (∑α)X/∑α 40 ETA_Eta_B_RC η
20 ETA_Beta ∑β 41 ETA_EtaP_B_RC η′
21 ETA_BetaP ∑β′
local
local
F
F
B
R
F
local
local
R
F
B
F
local
F
B
F
local
local
F
B
local
local
/N
s
ns
ns(δ)
v
/N
s
ns
ns(δ)
Y
X
v
/∑α
/∑α
v
66
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The “ETA” Indices in QSAR/QSPR/QSTR Research
Table 8. An overview of the modeling studies performed employing ETA indices*
Sl.
No.
Studied
Relationship
Endpoint Type of
1 QSTR Toxicity towards
Tetrahymena
pyriformis
2 QSTR Toxicity towards
fish
3 QSTR Toxicity towards
Tetrahymena
pyriformis
4 QSTR Acute Toxicity
towards Vibrio
fischeri
5 QSTR Acute Toxicity
towards Vibrio
fischeri
6 QSTR Acute Toxicity
towards Rana
japonica
Chemicals
Substituted
phenols
Substituted
benzenes
(phenols,
anilines, other
hydrocarbons)
Nitroaromatic
compounds
Phenylsulfonyl
carboxylates
Phenylsulfonyl
carboxylates
Benzene
derivatives
No. of
Samples
=50 Principal
n
total
Chemometric
Operation
component
Representative
Q2=0.945,
R
factor analysis,
MLR
n
=92 Principal
total
component
factor analysis,
MLR,
all-possible-
R2=0.885,
R
Q2=0.865, F
(df) = 92.6 (7,
84), s=0.230
subsets
regression.
n
=42 Principal
total
component
factor analysis,
MLR
R2=0.920,
R
Q2=0.880, F
(df) = 101.4 (4,
37), s=0.22
n
=56 Principal
total
component
factor analysis,
MLR
R2=0.852,
R
Q2=0.726, F
(df) = 57.4 (5,
50), s=0.186
n
=56 GFA R2=0.873,
total
R
Q2=0.771, F
(df) = 69.0 (5,
50), s=0.172
n
=51 GFA, FA,
total
MLR, PCRA
R2=0.915,
R
Q2=0.847, F
(df) = 65.841
(7, 43),
s=0.183
Value of
Metrics
2
=0.950
a
2
=0.876,
a
2
=0.910,
a
2
=0.837,
a
2
=0.861,
a
2
=0.901,
a
Chemical
Attributes
Explored using
ETA Indices
The factors increasing
the toxicity of
phenols are molecular
bulk, branching,
ηR, η′F, Σβ′ns, ETA
functionality of
phenolic O.
The positive
contribution of
molecular bulk,
presence of chloro,
hydroxy, methyl and
nitro substituents.
Presence of fluoro,
ether functionality,
amino or nitro
functionality in
an otherwise
unsubstituted ring,
nitro group ortho
to choloro reduces
toxicity.
Molecular bulk
(size), halogen and
additional nitro
group in the system
increases toxicity
while toxicity gets
decreased by the
presence of methyl
and hydroxymethyl
substituent.
The negative
impact of steric
bulk, branching,
chloro substituent,
unsaturation,
electronegative atoms
towards toxicity.
The parameters
reducing toxicity
are steric bulk,
functionality
contribution, chloro
group, nitro group.
Toxicity is
parabolically
related to
molecular size,
increases with the
presence of chloro
substituent, while
gets reduced due
to the occurrence
of groups like
methoxy, hydroxy,
carboxy, amino.
Ref.
Roy &
Ghosh,
2003
Roy &
Ghosh,
2004a
Roy &
Ghosh,
2004b
Roy &
Ghosh,
2004c
Roy &
Ghosh,
2005
Roy &
Ghosh,
2006a
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continued on following page
67
Table 8. Continued
The “ETA” Indices in QSAR/QSPR/QSTR Research
Sl.
No.
Studied
Relationship
Endpoint Type of
7 QSTR Nonspecific
toxicity towards
Saccharomyces
cerevisiae
8 QSPR n-Octanol/
water partition
coefficient
9 QSPR Bioconcentration
factors in fish
10 QSTR Inhibition
of seed
germination
rate of Cucumis
sativus
11 QSTR Toxicity towards
Chlorella
vulgaris
Chemicals
Substituted
benzene
derivatives
Non-ionic
organic
compounds
Non-ionic
organic
compounds
Substituted
phenols
Diverse
functional
organic
compounds
No. of
Samples
=51 FA, PCA,
n
total
Chemometric
Operation
stepwise MLR,
PLS
n
=122;
total
n
=92,
train
n
=30
test
k-means
clustering,
principal
component
factor analysis,
MLR, PLS,
stepwise MLR,
PCRA
=122 Principal
n
total
component
factor analysis,
MLR, PLS,
stepwise MLR,
PCRA, k-means
clustering
n
=41 Stepwise MLR,
total
FA-MLR,
GFA-MLR, G/
PLS, FA-PLS,
PCRA
n
=91,
total
n
=68,
train
=23
n
test
k-means
clustering,
stepwise MLR,
FA-MLR, PLS,
PCRA
Representative
Value of
Metrics
R2=0.884,
2
R
=0.874,
a
Q2=0.851, F
(df) = 87.9 (4,
46), s=0.235
Total:
2
=0.960,
R
Q2=0.953
Divided:
R2=0.974,
Q2=0.970,
2
R
=0.906
pred
R2=0.948,
2
R
=0.944,
a
Q2=0.847, F
(df) = 254.9 (8,
113), s=0.362
R2=0.825,
2
R
=0.811,
a
Q2=0.719,
PRESS=2.065
Total:
2
R
=0.928,
2
=0.925,
R
a
2
Q
=0.913, F
(df) = 275.29
(4, 86)
Divided:
2
=0.929,
R
2
Q
=0.900,
2
=0.870
R
pred
Chemical
Ref.
Attributes
Explored using
ETA Indices
The negative
contributions of
amino & carboxylic
acid functionalities
on the benzene ring
and occurrence of
electronegative atom
towards the toxicity
while positive
contributions of
presence of chloro
group and molecular
branchedness.
Partion coefficient
increases in the
presence of molecular
bulk and degree of
halogen substitution
while hydrogen
bonding or polar
interaction reduces it.
Presence of nitro,
amino, hydroxyl
groups reduces BCF,
while it increases
by branchedness,
molecular volume,
and chloro
substituents.
Toxicity
proportionately
increases in presence
of branching,
unsaturation and
groups like nitro,
cholo, bromo while
functionalities
namely hydroxyl,
carboxy, methoxy,
ortho-methyl exerts
negative impacts.
Furthermore, toxicity
was also found to
possess a non-linear
relationship with ETA
volume parameter
∑α.
Molecules
possessing
higher amount of
Roy &
Sanyal,
2006
Roy et al.,
2006a
Roy et al.,
2006b
Roy &
Ghosh,
2006b
Roy &
Ghosh,
2007
molecular bulk
(∑α), electron
richness (∑β ns)
and lipophilic
substituents like
chloro or bromo
are more toxic.
continued on following page
68
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The “ETA” Indices in QSAR/QSPR/QSTR Research
Table 8. Continued
Sl.
No.
Studied
Relationship
Endpoint Type of
12 QSTR Human toxicity
data (blood/
serum conc.)
13 QSTR Cytotoxicity in
rat hepatocytes
14 QSTR Toxicity towards
Tetrahymena
pyriformis
+
15 QSTR hERG K
channel blocking
activity
Chemicals
Organic
compounds
including
pharmaceuticals
NSAIDs n
Diverse
aromatic
compounds
Diverse
functional drugs
No. of
Samples
=26,
n
total
n
=19,
train
n
=7
test
Chemometric
Operation
Stepwise
MLR, k-means
clustering, FAMLR, GFA,
PLS, G/PLS,
PCRA
=15 Stepwise MLR,
total
PCRA, PLS,
GFA, G/PLS
n
n
n
n
n
n
total
train
test
total
train
test
=384,
=288,
=96
=67,
=50,
=17
Stepwise
MLR, k-means
clustering, FAMLR, PLS,
Stepwise
MLR, k-means
clustering, FAMLR, PLS
Representative
Value of
Metrics
Total:
R2=0.903,
2
R
=0.895,
a
Q2=0.834, F
(df) = 106.88
(2, 23)
Divided:
R2=0.724,
Q2=0.639,
2
R
=0.655
pred
R2=0.919,
2
R
=0.906,
a
2
Q
=0.854, F
(df) = 67.92
(2, 12)
Divided:
R2=0.854,
2
R
=0.846,
a
Q2=0.821,
2
R
=0.679
pred
Divided:
R2=0.658,
2
R
=0.619,
a
Q2=0.540,
2
R
=0.617
pred
Chemical
Attributes
Explored using
ETA Indices
Molecular bulk,
cholo group
(lipophilicity),
heteroatom within
a chain or ring,
unsaturation
increase the
toxicity while
hydroxyl group
and branching
reduce it.
Toxicity increases
with molecular
bulk and degree
of branching.
Unsaturation and
heteroatom content
have additional
impact on toxicity.
The parameters
important for
the prediction
of toxicity
are molecular
bulk, halogen
functionality
representing
lipophilicity and
nitrogen containing
functionality
giving an account
of polarity.
Electron-richness
and volume
increase hERG K+
channel blocking
activity while
groups/fragments
containing
aliphatic nitrogen
atom or –COOH
reduce it. The
parameter ∑α
additionally
showed a parabolic
relationship with
the modeled
toxicity endpoint.
Ref.
Roy &
Ghosh,
2008
Roy &
Ghosh,
2009a
Roy &
Ghosh,
2009b
Roy &
Ghosh,
2009c
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continued on following page
69
Table 8. Continued
The “ETA” Indices in QSAR/QSPR/QSTR Research
Sl.
No.
Studied
Relationship
Endpoint Type of
16 QSTR Toxicity towards
Tetrahymena
pyriformis
17 QSPR Octanol-water
partition
coefficient
QSPR Octanol-water
partition
coefficient
QSPR Aqueous
solubility
QSPR Molar
refractivity (R
m
QSPR Aromatic
substituent
constant π
Aromatic
aldehydes
Diverse
functional
organic
compounds
Aliphatic
and aromatic
compounds
Aliphatic
and aromatic
compounds
Aliphatic
)
and aromatic
compounds
Aromatic
substituents
Chemicals
No. of
Samples
=77,
n
total
n
=50,
train
n
=17
test
=168,
n
total
n
=84,
train
n
=84
test
=139,
n
total
n
=70,
train
n
=69
test
n
=193,
total
n
=97,
train
n
=96
test
n
=166,
total
n
=83,
train
n
=83
test
n
=98,
total
n
=50,
train
n
=48
test
Chemometric
Operation
Stepwise MLR,
GFA, G/PLS
Stepwise MLR,
PLS
Representative
Value of
Metrics
R2=0.895,
Q2=0.870,
2
R
=0.902,
pred
r
=0.896
m2(test)
Total (PLS):
R2=0.943,
2
R
=0.940,
a
Q2=0.933
Divided (PLS):
R2=0.922,
Q2=0.903,
2
R
=0.919
pred
Total (PLS):
R2=0.992,
2
R
=0.991,
a
2
Q
=0.989
Divided (PLS):
2
=0.988,
R
Q2=0.984,
2
R
=0.972
pred
Total (PLS):
R2=0.951,
2
R
=0.948,
a
2
Q
=0.937
Divided (PLS):
2
=0.918,
R
Q2=0.897,
2
R
=0.872
pred
Total (PLS):
R2=0.990,
2
R
=0.990,
a
Q2=0.989
Divided (PLS):
R2=0.980,
Q2=0.978,
2
R
=0.993
pred
Total (PLS):
R2=0.770,
2
R
=0.755,
a
Q2=0.718
Divided (PLS):
R2=0.795,
Q2=0.736,
2
R
=0.540
pred
Chemical
Attributes
Explored using
ETA Indices
Molecular bulk,
electronegative
substituents,
lipophilic
substituent increase
the toxicity
of aromatic
aldehydes. The
keto oxygen in
aldehyde group
also imparts their
effect towards
toxicity.
Lipophilicity increases
with molecular
bulk while it tends
to decrease in the
presence of branching,
relative unsaturation
content and hydrogen
bonding propensity
measure.
Different electronic
features, unsaturation
and shape parameters
were important for the
partition coefficient of
aliphatic and aromatic
compounds.
Hydrogen bonding
propensity and H-bond
donor atoms were
observed to be the
important features
for modeling aqueous
solubility. However,
relative unsaturation
content and
branchedness were also
contributing.
Electronegativity,
molecular bulk and
hydrogen bonding
propensity were
contributing factors
towards modeling of
molar refraction of
chemicals.
Measure of H-bond
acceptor atoms or
polar surface area,
H-bond donor atoms,
H-bonding propensity,
electron-richness,
electronegativity,
molecular bulk and
branching were the
contributing parameters
for modeling π.
Ref.
Roy &
Das, 2010
Roy &
Das,
2011a
Roy &
Das,
2011a
Roy &
Das,
2011a
Roy &
Das,
2011a
Roy &
Das,
2011a
70
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continued on following page
The “ETA” Indices in QSAR/QSPR/QSTR Research
Table 8. Continued
Sl.
No.
Studied
Relationship
Endpoint Type of
QSPR Aromatic
substituent
constant MR
QSPR Aromatic
substituent
constant σ
m
QSPR Aromatic
substituent
constant σ
p
18 QSTR Toxicity towards
Pimephales
promelas
19 QSPR Critical micelle
concentration
(CMC)
21 QSPR Critical micelle
concentration
(CMC)
Chemicals
Aromatic
substituents
Aromatic
substituents
Aromatic
substituents
Diverse organic
chemicals
Non-ionic
surfactants
Cationic
surfactants
No. of
Samples
=99,
n
total
n
=50,
train
n
=49
test
n
=97,
total
n
=49,
train
n
=48
test
n
=99,
total
n
=50,
train
n
=49
test
=459,
n
total
n
=344,
train
n
=115
test
=54,
n
total
n
=41,
train
n
=13
test
=35,
n
total
n
=26,
train
n
=9
test
Chemometric
Operation
k-means
clustering,
Stepwise MLR,
GFA
Stepwise MLR,
GFA, PLS
k-means
clustering,
stepwise MLR,
GFA, ANN
Representative
Value of
Metrics
Total (PLS):
R2=0.986,
2
=0.985,
R
a
2
Q
=0.979
Divided (PLS):
R2=0.977,
Q2=0.967,
2
=0.965
R
pred
Total (PLS):
R2=0.766,
2
R
=0.758,
a
Q2=0.735
Divided (PLS):
R2=0.750,
Q2=0.677,
2
R
=0.736
pred
Total (PLS):
R2=0.726,
2
R
=0.717,
a
Q2=0.682
Divided (PLS):
R2=0.670,
Q2=0.582,
2
R
=0.585
pred
Divided
(GFA):
R2=0.763,
Q2=0.751,
2
R
=0.783,
pred
r
=0.777
m2(test)
Divided (PLS):
R2=0.907,
Q2=0.879,
2
R
=0.923,
pred
r
=0.912
m2(test)
Divided
(GFA):
R2=0.949,
Q2=0.893,
2
R
=0.904,
pred
r
=0.892
m2(test)
Chemical
Attributes
Explored using
ETA Indices
MR was related
to the impact of
molecular bulk,
relative unsaturation,
electron richness,
electronegativity,
H-bonding atom count,
H-bonding propensity,
resonating lone electron
pair and branching of
the molecules.
σm is encoded by
shape parameter,
electronegativity,
relative unsaturation
content, resonating
lone electron pair,
and measure of nonhydrogen bonding
bulky heteroatom.
σp can be described
in terms of
molecular branching,
electronegativity,
resonating lone
electron pair, relative
unsaturation content
and molecular bulk.
Apart from the
positive impact
of the computed
lipophilicity
measure, measures
of molecular
bulk, electron
richness, relative
unsaturation
content, and
branchedness
are necessary
attributes.
logCMC value
increases with
molecular shape,
heteroatom content
while molecular
bulk and branching
tends to reduce it.
Hydrogen bond
donor atoms,
molecular size
and degree of
unsaturation
affect the CMC
of cationic
surfactants.
Ref.
Roy &
Das,
2011a
Roy &
Das,
2011a
Roy &
Das,
2011a
Roy &
Das, 2012
Roy &
Kabir,
2012a
Roy &
Kabir,
2012b
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continued on following page
71
Table 8. Continued
The “ETA” Indices in QSAR/QSPR/QSTR Research
Sl.
No.
Studied
Relationship
Endpoint Type of
20 QSPR Critical micelle
concentration
(CMC)
22 QSPR Aqueous
solubility
23 QSTR Toxicity towards
rodent and
interspecies
toxicity
correlation
among daphnids,
fish and algae.
24 QSTR Ecotoxicity
towards Vibrio
fischeri
Chemicals
Anionic
surfactants
Drug like
molecules and
agrochemicals
Samples
=37,
n
total
n
=28,
train
n
=9
test
=565,
n
total
n
=282,
train
n
=283
test
Pharmaceuticals Rodent:
No. of
=102,
n
train
n
=34
test
Daphniaalgae:
=65,
n
train
n
=24
test
Fish-algae:
=50,
n
train
n
=22
test
Diverse ionic
liquids
LDA:
=110,
n
train
n
=37
test
MLR:
n
=90,
train
n
=36
test
Chemometric
Operation
Stepwise MLR,
GFA, ANN,
PCA
Stepwise MLR,
GFA, G/PLS
Stepwise MLR,
PLS
k-means
clustering,
LDA, stepwise
MLR, GFA
Representative
Value of
Metrics
Divided
(GFA):
R2=0.957,
Q2=0.938,
2
R
=0.923,
pred
r
=0.823
m2(test)
Divided (G/
PLS):
R2=0.802,
Q2=0.793,
2
R
=0.806,
pred
r
=0.735
m2(test)
Rodent:
2
R
=0.618,
Q2=0.556,
2
=0.550
R
pred
Daphniaalgae:
2
R
=0.650,
Q2=0.580,
2
=0.604
R
pred
Fish-algae:
R2=0.752,
2
Q
=0.654,
2
R
=0.696
pred
LDA:
Wilk’s
λ=0.298,
Rc=0.838,
MCC
=0.841
trn
MLR:
R2=0.694,
Q2=0.651,
2
R
=0.739,
pred
2
r
m ( )test
=0.623,
Δr
=0.156
m2(test)
Chemical
Ref.
Attributes
Explored using
ETA Indices
Electronegativity
and topological
environment
Roy &
Kabir,
2013
impart negative
contribution, while
lipophilicity and
branching show
positive impact
towards CMC of
anionic surfactants.
Lipophilicity,
unsaturation,
hydrogen bonding
Das &
Roy,
2013
propensity and
polar surface
area measure are
the responsible
chemical features
for logS.
Rodent toxicity is
chiefly influenced
Das et al.,
2013
by the charge
distribution and
heteroatom atom
count. Interspecies
correlation
models show
the influence of
different atom-type
AlogP fragments,
molecular shape,
charge distribution
and functionality
measure.
Branching,
molecular size and
solvation entropy
Das &
Roy,
2012
of cations along
with a lipophilicity
contribution of
the anions are
the contributing
chemical attributes
for eco-toxicity
of ILs towards V.
fischeri.
continued on following page
72
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The “ETA” Indices in QSAR/QSPR/QSTR Research
Table 8. Continued
Sl.
No.
Studied
Relationship
Endpoint Type of
25 QSTR Toxicity towards
Daphnia magna
26 QSTR Ecotoxicity
towards
Scenedesmus
vacuolatus
27 QSTR Inhibition of
Electrophorus
electricus AChE
enzyme
Chemicals
Diverse ionic
liquids
Diverse ionic
liquids
Diverse ionic
liquids
No. of
Samples
LDA:
=46,
n
train
n
=16
test
MLR:
n
=35,
train
n
=14
test
LDA:
=42,
n
train
n
=18
test
MLR:
n
=35,
train
n
=14
test
LDA:
=182,
n
train
n
=110
test
MLR:
n
=148,
train
n
=84
test
Chemometric
Operation
k-means
clustering,
LDA, stepwise
MLR, PLS
LDA, stepwise
MLR, GFA,
k-means
clustering
k-means
clustering,
LDA, stepwise
MLR, GFA,
PLS, molecular
docking
Representative
Value of
Metrics
LDA:
Wilk’s
λ=0.257,
Rc=0.862,
MCC
=0.867,
trn
AUCROC=0.991
& 1.0
MLR:
(trn)
(test)
R2=0.948,
Q2=0.875,
2
R
=0.817,
pred
2
r
m ( )test
=0.802,
Δr
=0.099
m2(test)
LDA:
Wilk’s
λ=0.468,
Rc=0.729,
MCC
=0.802,
trn
AUCROC=0.918
& 1.0
MLR:
(trn)
(test)
R2=0.883,
Q2=0.829,
2
Q
=0.893,
(F1)
2
Q
=0.891,
(F2)
2
r
m ( )test
=0.796
LDA:
Wilk’s
λ=0.374,
Rc=0.791,
MCC
=0.802,
trn
AUCROC=0.959
& 0.914
MLR:
(trn)
(test)
R2=0.838,
Q2=0.808,
2
Q
=0.822,
(F1)
RMSEp=0.248,
2
r
m ( )test
=0.762
Chemical
Attributes
Explored using
ETA Indices
Lipophilicity,
volume of
heteroatoms
electronegativity,
presence of long
cationic side
chains, terminal
branches and
branching pattern
at terminal atoms
as well as at the
carbon atom nearer
to heteroatomic
substitution are
the contributing
chemical features.
Presence of bulky
and multiple bonded
heteroatom causing
nucleophilic addition
reaction is responsible
for the toxicity. Long
alkyl side chain on
cationic N atom also
increases toxicity.
Toxicity can decrease
by incorporation of
H-bond donor group
like hydroxyl (−OH).
Increased toxicity is
caused by longer alkyl
chain length on N
atom providing proper
spatial conformation,
presence of quaternary
N atom in ring allowing
π-cationic interaction,
and distributed electron
density in cations due
to resonating electrons.
Lipophilicity of anion
additionally account
for the toxicity, i.e.,
increased enzyme
inhibition. However,
presence of long chains
on free N+/P+ causes
reduction of the toxicity
due to free rotation of
chains causing binding
hindrance. Toxicity
also reduces due to the
presence of H-bond
donors & acceptors
blocking partitioning of
molecule.
Ref.
Roy &
Das, 2013
Das &
Roy,
2014a
Das &
Roy,
2014b
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continued on following page
73
Table 8. Continued
The “ETA” Indices in QSAR/QSPR/QSTR Research
Sl.
No.
Studied
Relationship
Endpoint Type of
Chemicals
28 QSPR Adsorption Organic
compounds
29 QSPR Bio-
concentration
Diverse
chemicals
factor
30 QSPR Odor threshold Aliphatic
alcohols
*Full form of some abbreviations used: MLR: Multiple linear
regression; GFA: Genetic function approximation; FA: Factor
analysis; PCA: Principal component analysis; PCRA: Principalcomponent regression analysis; PLS: Partial least squares; LDA:
Linear discriminant analysis.
No. of
Samples
=3483,
n
total
n
=2613,
train
n
=870
test
=522,
n
total
n
=324,
train
n
=198
test
=53,
n
total
n
=42,
train
n
=11
test
Chemometric
Operation
k-means
clustering,
PCA, stepwise
MLR, PLS
k-means
clustering,
GFA, PLS
k-means
clustering,
GFA, G/PLS
Representative
Value of
Metrics
Divided:
R2=0.815,
Q2=0.806,
2
r
m ( )LOO
=0.722,
2
Q
=0.791,
(F1)
2
r
m ( )test
=0.719
Divided:
R2=0.614,
2
R
=0.611,
a
2
=0.597,
Q
2
R
=0.696,
pred
2
r
m ( )test
=0.580
Divided:
R2=0.809,
Q2=0.778,
2
R
=0.813,
pred
2
r
m ( )test
=0.679
Chemical
Attributes
Explored using
ETA Indices
The adsorption
behavior is
characterized
by molecular
volume, H-bond
donor groups,
heteroatomic
contribution,
electronegativity,
shape, H-bonding
propensity and
unsaturation.
Occurrence of
fused ring systems
and halogen atoms
exert positive
contribution while
electronegativity,
H-bond donor
groups, polarity
and lowered
partition
coefficient have
negative impact on
BCF.
Odor threshold
is potentiated
by increased
hydrophobicity
and reduced
electronegativity.
Ref.
Ray &
Roy,
2013
Pramanik
& Roy,
2014
Pal et al.,
2014
74
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The “ETA” Indices in QSAR/QSPR/QSTR Research
presence of polar functional groups like hydroxy, methoxy, carboxy etc. In cases of ionic liquids, the
toxicity is proportionately related to the chain length of the cationic alkyl chain. However, the same is
not applicable in case of modeling AChE enzyme inhibition of eel where the head group is supposed to
provide a specific spatial arrangement of the cation containing aromatic core and not for the ammonium
or phosphonium core. The QSPR studies show the impact of the ETA molecular bulk parameter which
relates with increased partitioning and decreased surfactancy behavior. The ETA parameters also show
molecular branching and hydrogen bonding natures to be related with reduced octanol-water partitioning nature. The H-bonding phenomenon is largely attributed to the presence of polar groups like –OH,
–O–, –NH
, –NH– etc. However, the ETA H-bonding parameter ψ shows a negative contribution while
2
modeling aqueous solubility of drug like compounds and agrochemicals and that has been justified by
cohesive rigidity of such complex molecules due to intra- and inter-molecular H-bonding. The ETA
indices for electron richness, branching and resonating lone electron pair are found to be correlated with
aromatic substituent constants.
CONCLUSION AND FUTURE AVENUES
The aim of any scientific discipline lies in unraveling the reason hidden inside a system. Since, natural
processes are defined by perspectives belonging to different disciplines, interdisciplinary studies are always
helpful in deriving logical decisions. Predictive modeling studies aid in constituting rational basis for the
physical, chemical as well as biological manifestations of chemical compounds. The chemistry suitably
aided by concepts of mathematics and biology helps in unfolding the behavioral pattern of chemicals.
Mathematical descriptors are the logical basis for the exploration of the chemical fraternity. Since the
introduction of predictor variables, a long way has already been traversed in depicting the chemical
behavior and it has been envisaged that theoretically derived chemical descriptors are more than just
simple quantification of chemical structures to derive correlations. Since, there is an amalgamation of
chemical principles with mathematical algorithms, care should be taken that the encoded information
is also helpful in deriving mechanistic interpretation instead of developing only correlations with the
endpoint under investigation.
There is also a good amount of arguments regarding the dimensionality of analysis during the development of predictive correlation models. It is quite obvious that two-dimensional analyses are relatively
easier to perform and give reproducible results. However, the molecular properties and interactions are
also believed to be affected by features like shape, surface area and volume which are three dimensional.
Hence, it becomes very interesting if a theoretical basis is generated to incorporate information of threedimensional perspectives in two-dimensional indices. It may be observed that there is a good correlation
and inheritance between these two and three- dimensional features since the features like size, shape,
volume etc. are in turn controlled by the property of the individual atoms and bonds constituting a molecule. The three-dimensional geometry is also referred to as molecular topography (Kier & Hall, 1986)
and studies have shown the occurrence of quantum chemical basis (Stankevich et al., 1995) as well as
Hückel’s molecular orbital (Gálvez et al., 1998) features in the topological parameters rendering them
reliable predictor parameters in extracting powerful chemical information.
The ETA indices are enriched since they do not rely only on topological distance based measures
of hydrogen suppressed molecular graphs. This group of parameters are advantageous as they encode
suitable chemical information on different aspects of molecular chemistry, e.g., volume, shape, size,
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75