Journal of Environmental Science and Health Part A (2007) 42, 573–590
C Taylor & Francis Group, LLC
Copyright ISSN: 1093-4529 (Print); 1532-4117 (Online)
DOI: 10.1080/10934520701244326
Quantitative structure—activity relationships
for the prediction of relative in vitro potencies (REPs)
for chloronaphthalenes
TOMASZ PUZYN1 , JERZY FALANDYSZ1 , PAUL D. JONES2 and JOHN P. GIESY2,3,4
1
Department of Environmental Chemistry and Ecotoxicology, University of Gdańsk, Faculty of Chemistry, Gdańsk, Poland
Department of Veterinary Biomedical Sciences and Toxicology Centre, University of Saskatchewan, Saskatoon,
Saskatchewan, Canada
3
Zoology Department, National Food Safety and Toxicology Centre and, Centre for Integrative Toxicology,
Michigan State University, East Lansing, Michigan, USA
4
Department of Biology and Chemistry, City University of Hong Kong, Kowloon, SAR, China
2
Chloronaphthalenes (CNs), due to their structural similarities to 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) and the other “dioxinlike”compounds, can bind to the aryl hydrocarbon receptor (AhR) and induce a wide range of pleotrophic effects. Relative potency
of individual dioxin analogues can be measured relative to that of TCDD. Relative effects potencies (REP) can be based on many
responses, including in vivo and in vitro responses. Both in vivo and in vitro tests, based on either indigenous responses such as the
induction of ethoxyresorufin O-deethylase (EROD) or exogenous reporter genes under the control of the AhR such as luciferase can
be used to determine REP values. Here we used measured REP values determined for CNs in two assays. Both assays are based on
H4IIE rat hepatoma cells. The H4IIE assay is based on expression of the endogenous reporter gene (CYP-1A) that codes for the
expression of EROD and the H4IIE-luc assay which is based on the exogenous reporter gene (luciferase) transfected into the H4IIE
cell line. Experimentally determined REP were available for only 17 and 18 of the 75 possible choronaphthalene congeners, for the
H4IIE and H4IIE-luc assays, respectively. For this reason computational models were developed to allow prediction of the relative
potencies of the other CN congeners. Predictive relationships were based on quantum chemical descriptors obtained from Density
Functional Theory (DFT) calculations (B3LYP/6–311++G∗∗ ). The final models were found by means of a hybrid method combining
a genetic algorithm and artificial neural networks. REP values estimated for individual CNs based on the H4IIE assay ranged from
4.3 × 10−9 to 3.2 × 10−2 while those based on the H4IIE-luc assay ranged from 4.0 × 10−8 to 1.8 × 10−3 . CN congeners nos. 66,
67, 70 and 73 were exhibited the greatest REP values in both assays. The 1,2,3,5,6,8-hexaCN congener (no. 68) had a REP value that
was 10-fold less. The remaining congeners had REP values that were less or did not cause sufficient up-regulation of the monitored
genes to allow for the calculation of a REP. Interactions of CNs with the AhR could be affected by three possible factors: molecular
size, steric interactions and electrostatic interactions. These findings are discussed relative to the use of consensus TCDD equivalency
factors’ (TEFs) for use in risk assessments of CNs for regulatory purposes.
Keywords: Chloronaphthalenes, REPs, EROD, H4IIe, H4IIE-luc, luciferase, QSAR, dioxin-like compounds, PCNs.
Introduction
Chloronaphthalenes (CNs) are a class containing 75
individual compounds (congeners) differing by a degree
of chlorination (from mono- to octachloronaphthalene)
and position of Cl substitution.[1−3] When substituted with
Address correspondence to Jerzy Falandysz, Department of Environmental Chemistry & Ecotoxicology, University of Gdańsk,
18 Sobieskiego Str., PL 80-952, Gdańsk, Poland; E-mail:
jfalandy@chem.univ.gda.pl
Received December 29, 2006.
more than one chlorine atom, these compounds are referred
to as polychlorinated napthalenes (PCNs). Chloronaphthalenes are relatively well known persistent organic pollutants and have been intensively studied.[4−6] CNs have
been released into the environment from use of technical CN mixtures and as a byproduct in chlorobiphenyl
formulations. CNs has been used for numerous applications, including electro energetic equipment, like transformers and capacitors. Although CNs are formed also in
thermal processes such as combustion, incineration etc.,
the main sources of the compounds are related to human activities.[1,7−11] CNs have been found in many environmental matrices, including wildlife and humans.[1,5,12,13]
574
Technical CN formulations, such as the Halowaxes, can be
toxic to biota.[14]
The critical (occurring at the least concentration) mechanism of toxic action for CNs is similar to that of “dioxinlike” compounds. Due to their structural similarities to
2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), can bind to
the aryl hydrocarbon receptor (AhR). The AhR is a cytoplasmic receptor that after formation of the CN-AhR complex and binding the nuclear translocator protein (ARNT),
is translocated to the nucleus, where after some transformations the complex interacts with specific DNA regions,
known as dioxin response elements (DREs) that control the
expression of many genes that are indicative of exposure to
“dioxin-like compounds.”[15−17]
Based on the knowledge of the toxic mechanism of action, several in vitro bioassays based on mammalian cell
cultures have been developed. These assays allow the determination of relative potency values (REP) for compounds
that can cause AhR-mediated effects by comparing the
amount of the chemical required to cause the same level
of response (such as gene induction) as the reference compound, TCDD.[18] These assays can be based on both endogenous and exogenous reporter genes. Both of the assays
used to generate measured REP values for CNs that were
used in the Quantitative Structure– Activity Relationship
(QSAR) models developed in this study are based on H4IIE
rat hepatoma cells. One commonly used assay is the H4IIE
assay, which is based on the expression of (CYP-1A1),
which is the gene that codes for the enzyme that catalyzes
the de-alkylation of ethoxy-resorufin (7-ehoxyrezorufin Odeethylaze (EROD).[19,20] The H4IIE assay has several limitations to its use.[20,21]
Some of the inherent limitations of using endogenous reporter genes are situations, such as the conditions where
the ligand of interest is a suicide substrate for the reporter
gene or where the ligands are only partial agonists for the
AhR. In such cases, the results of the H4IIE assay are
unreliable.[17,20,21] Alternatively, the use of H4IIE-luc assay is more sensitive and avoids some of H4IIE assay. The
H4IIE assay is a genetically engineered version of the H4IIE
cells, into which an exogenous gene that codes for the enzyme luciferase. Luciferase is the enzyme that produces light
in firefly tails. This gene has been inserted under the control of a DRE.[21] Synthesis of luciferin in response to exposure to AhR-active compounds results in changes in the
production of light that can be a sensitive measure under
appropriate conditions.[22]
REP values have been determined for 18 chloronaphthalene congeners in the H4IIE (EROD) assay, while REP
values have been determined for 17 congeners by use of
the H4IIE-luc (luciferase) assay.[17,19] However, risk assessments of CN mixtures has been limited by the lack of REP
values for the other CN congeners.[23−29] Thus, in this study,
we developed quantitative relationships, based on the structure of the congeners, to predict REP values for those congeners for which REPs were not available. These QSARs
Puzyn et al.
were based on the assumption that differences in REP values are a function of the molecular structure and that a
predictive relationship, based on first and second principles can be developed that would be predictive of the magnitude of the REP. The descriptors applied were calculated
from quantum-chemical Density Functional Theory, and
the final QSAR models were developed based on a hybrid
method that made use of both a genetic algorithm and artificial neural networks (GA-ANN). Several examples of the
use of QSAR to estimate REP values for CNs are available
in the literature.[30,31]
Both studies were based on molecular descriptors calculated based on lower level of quantum-chemical theory and
used linear predictive relationships. However, since the relationships between molecular structure and REP values of
CNs are non-linear, and the fact that the magnitudes of differences in values of molecular descriptors were small the
methodology applied in this study was expected to provide
more accurate results. The aims of the presented study were
to: (i) to predict REP values of all individual CNs based on
the GA-ANN hybrid approach with molecular descriptors
from DTF/6–311++G∗∗ calculations; (ii) to compare the
REP values obtained from the predictive relationships with
values measured in vitro in previous studies; (iii) to provide
guidance on the use of REP values for CN congeners to be
further evaluated in vitro and in vivo and the use of REP
values in risk assessments and; (iv) to propose first-ever
toxic equivalency factors (TEFs) for all of chloronaphthalene congeners.
Materials and methods
The predictive and validation steps were conducted in several phases. The same modeling strategy was used with both
the data from the H4IIE and H4II-luc assays. The empirical
data from both in vitro assays was used to develop the predictive relationships then predicted values were compared
to the empirical data (Table 1). For both assays CN congeners, for which experimental data was available, were divided into two sets: a training set (TS) and an independent
validation set (VS). Optimized predictive relationships were
used to make reliable predictions for each of non-in vitrotested CN congeners and, in this way, we finally obtained a
complete activity data table for all 75 congeners. The applicability domain of the model was evaluated by use of principal component analysis of the rotated feature (descriptor)
space and ranges of available empirical data.[32−34] These
data ranged from 2.1 × 10−3 to 3.1 ×10−9 and from 3.9 ×
10−3 to 1.0 × 10−7 for H4IIE and H4II-luc, respectively.
In the first stage of the study 40 molecular descriptors (Table 2 and Appendix) were calculated for each of
the congeners. These quantum-mechanical computations
were conducted at the level of Density Fucntional Theory by use of the Gaussian 03 software package.[35] We
used one of the most advanced DFT hybrid functional
575
The prediction of relative potencies for chloronaphthalenes
Table 1. Experimental and estimated REP values of activity of CNs based on the H4IIE (EROD) and H4IIE-luc assays.
H4IIE EROD
H4IIE-luc
CN
Congener
In vitroa,b
In silicoc
In silicod
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
1-chloronaphthalene
2-chloronaphthalene
1,2-dichloronaphthalene
1,3-dichloronaphthalene
1,4-dichloronaphthalene
1,5-dichloronaphthalene
1,6-dichloronaphthalene
1,7-dichloronaphthalene
1,8-dichloronaphthalene
2,3-dichloronaphthalene
2,6-dichloronaphthalene
2,7-dichloronaphthalene
1,2,3-trichloronaphthalene
1,2,4-trichloronaphthalene
1,2,5-trichloronaphthalene
1,2,6-trichloronaphthalene
1,2,7-trichloronaphthalene
1,2,8-trichloronaphthalene
1,3,5-trichloronaphthalene
1,3,6-trichloronaphthalene
1,3,7-trichloronaphthalene
1,3,8-trichloronaphthalene
1,4,5-trichloronaphthalene
1,4,6-trichloronaphthalene
1,6,7-trichloronaphthalene
2,3,6-trichloronaphthalene
1,2,3,4-tetrachloronaphthalene
1,2,3,5-tetrachloronaphthalene
1,2,3,6-tetrachloronaphthalene
1,2,3,7-tetrachloronaphthalene
1,2,3,8-tetrachloronaphthalene
1,2,4,5-tetrachloronaphthalene
1,2,4,6-tetrachloronaphthalene
1,2,4,7-tetrachloronaphthalene
1,2,4,8-tetrachloronaphthalene
1,2,5,6-tetrachloronaphthalene
1,2,5,7-tetrachloronaphthalene
1,2,5,8-tetrachloronaphthalene
1,2,6,7-tetrachloronaphthalene
1,2,6,8-tetrachloronaphthalene
1,2,7,8-tetrachloronaphthalene
1,3,5,7-tetrachloronaphthalene
1,3,5,8-tetrachloronaphthalene
1,3,6,7-tetrachloronaphthalene
1,3,6,8-tetrachloronaphthalene
1,4,5,8-tetrachloronaphthalene
1,4,6,7-tetrachloronaphthalene
2,3,6,7-tetrachloronaphthalene
1,2,3,4,5-pentachloronaphthalene
1,2,3,4,6-pentachloronaphthalene
1,2,3,5,6-pentachloronaphthalene
1,2,3,5,7-pentachloronaphthalene
1,2,3,5,8-pentachloronaphthalene
1,2,3,6,7-pentachloronaphthalene
1,2,3,6,8-pentachloronaphthalene
1,2,3,7,8-pentachloronaphthalene
1,2,4,5,6-pentachloronaphthalene
1.1 × 10−07TS
8.9 × 10−10
1.1 × 10−08
2.1 × 10−06
2.5 × 10−11
5.0 × 10−08
3.5 × 10−06
1.3 × 10−07
7.1 × 10−11
1.9 × 10−07
5.1 × 10−06
2.0 × 10−06
6.9 × 10−07
6.0 × 10−06
2.8 × 10−10
1.4 × 10−06
1.4 × 10−06
1.1 × 10−07
1.6 × 10−06
6.5 × 10−11
1.1 × 10−09
1.9 × 10−09
3.2 × 10−12
2.0 × 10−08
2.5 × 10−10
4.0 × 10−08
1.1 × 10−05
4.1 × 10−05
4.7 × 10−06
6.0 × 10−05
7.8 × 10−06
3.9 × 10−05
5.5 × 10−08
2.0 × 10−07
2.6 × 10−07
8.7 × 10−08
1.3 × 10−04
2.4 × 10−07
2.9 × 10−04
1.2 × 10−06
4.0 × 10−07
5.1 × 10−05
2.5 × 10−06
5.1 × 10−09
7.2 × 10−06
4.7 × 10−07
6.0 × 10−05
5.4 × 10−06
5.5 × 10−04
3.2 × 10−05
1.7 × 10−05
1.9 × 10−05
3.2 × 10−05
1.6 × 10−05
4.4 × 10−05
2.8 × 10−05
2.0 × 10−04
5.4 × 10−06
9.5 × 10−08
1.0 × 10−08
2.3 × 10−07
3.2 × 10−08
4.3 × 10−09
6.5 × 10−09
2.8 × 10−08
6.0 × 10−08
1.4 × 10−07
2.2 × 10−08
4.4 × 10−08
3.5 × 10−07
4.2 × 10−08
9.8 × 10−09
4.5 × 10−08
1.5 × 10−07
6.6 × 10−07
5.9 × 10−08
8.1 × 10−09
5.0 × 10−08
1.9 × 10−08
1.0 × 10−07
3.6 × 10−09
1.9 × 10−08
1.3 × 10−08
8.9 × 10−07
9.1 × 10−07
2.4 × 10−08
4.4 × 10−06
3.4 × 10−07
1.5 × 10−08
8.3 × 10−09
7.1 × 10−08
4.7 × 10−07
3.0 × 10−08
1.1 × 10−06
1.5 × 10−06
3.6 × 10−08
3.3 × 10−07
1.2 × 10−07
3.9 × 10−06
1.2 × 10−06
1.4 × 10−08
3.2 × 10−07
2.1 × 10−06
7.1 × 10−09
1.2 × 10−08
2.3 × 10−04
3.6 × 10−07
4.2 × 10−05
1.5 × 10−05
8.5 × 10−06
1.3 × 10−08
2.8 × 10−05
7.1 × 10−06
2.3 × 10−05
1.5 × 10−06
3.1 × 10−09VS
2.6 × 10–07TS
6.2 × 10−07TS
3.5 × 10−07TS
3.5 × 10−06TS
3.5 × 10−04VS
7.6 × 10−05TS
3.9 × 10−06TS
2.2 × 10–05TS
1.6 × 10−06VS
In vitrob,e
1.0 × 10−07TS
2.0 × 10−07TS
4.2 × 10−07VS
5.8 × 10−07TS
1.6 × 10−05TS
5.8 × 10−06TS
1.7 × 10−04TS
4.6 × 10−05VS
3.5 × 10−06VS
In silicof
In silicoc
In silicod
I
I
I
I
I
I
I
I
I
I
I
I
I
I
I
I
I
I
I
I
I
I
I
I
I
I
I
I
I
I
I
I
I
I
I
I
I
I
I
I
I
I
I
I
I
I
A
I
I
A
A
A
I
A
I
I
I
7.2 × 10−12
9.5 × 10−10
3.5 × 10−07
5.9 ×10−09
9.1 × 10−09
2.6 × 10−07
4.1 × 10−08
7.4 × 10−09
2.5 × 10−08
2.8 × 10−06
6.0 × 10−07
5.1 × 10−07
6.9 × 10−06
6.8 × 10−08
6.3 × 10−07
9.1 × 10−07
1.1 × 10−06
1.2 × 10−06
2.6 × 10−08
1.0 × 10−07
1.1 × 10−07
1.1 × 10−08
1.4 × 10−08
1.9 × 10−08
9.3 × 10−07
6.6 × 10−06
2.8 × 10−05
1.9 × 10−05
4.5 × 10−05
2.6 × 10−05
3.5 × 10−05
1.5 × 10−06
2.5 × 10−06
3.4 × 10−06
1.9 × 10−06
5.9 × 10−05
3.5 × 10−06
6.8 × 10−05
1.1 × 10−05
4.7 × 10−06
3.5 × 10−05
4.0 × 10−05
4.3 × 10−07
2.0 × 10−05
4.7 × 10−06
2.1 × 10−05
1.7 × 10−05
3.3 × 10−04
6.3 × 10−05
6.0 × 10−05
7.4 × 10−05
2.2 × 10−04
5.9 × 10−05
1.3 × 10−04
9.1 × 10−05
1.8 × 10−04
2.9 × 10−05
4.1 × 10−08∗
1.5 × 10−07
2.2 × 10−07
1.2 × 10−07
1.5 × 10−07
2.6 × 10−07
4.0 × 10−08∗
9.3 × 10−08∗
1.6 × 10−06
3.5 × 10−07
7.2 × 10−07
4.9 × 10−07
9.1 × 10−07
4.5 × 10−07
4.5 × 10−07
4.5 × 10−07
1.7 × 10−07
7.2 × 10−06
2.4 × 10−07
2.2 × 10−07
1.1 × 10−06
4.0 × 10−06
1.6 × 10−06
1.9 × 10−07
3.7 × 10−08
9.3 × 10−07
2.3 × 10−06
8.7 × 10−07
4.8 × 10−06
3.0 × 10−06
1.7 × 10−05
5.9 × 10−07
2.1 × 10−06
1.3 × 10−06
4.2 × 10−06
2.7 × 10−06
6.3 × 10−07
4.4 × 10−06
7.4 × 10−07
1.4 × 10−05
2.2 × 10−05
3.2 × 10−06
7.8 × 10−07
2.0 × 10−06
1.4 × 10−05
1.4 × 10−08
3.0 × 10−07
1.0 × 10−05
7.9 × 10−07
3.0 × 10−05
1.5 × 10−05
3.8 × 10−05
5.2 × 10−06
5.5 × 10−05
6.8 × 10−05
5.6 × 10−05
1.5 × 10−06
(Continued on next page)
576
Puzyn et al.
Table 1. Experimental and estimated REP values of activity of CNs based on the H4IIE (EROD) and H4IIE-luc assays. (Continued)
H4IIE EROD
CN
Congener
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
1,2,4,5,7-pentachloronaphthalene
1,2,4,5,8-pentachloronaphthalene
1,2,4,6,7-pentachloronaphthalene
1,2,4,6,8-pentachloronaphthalene
1,2,4,7,8-pentachloronaphthalene
1,2,3,4,5,6-hexchloronaphthalene
1,2,3,4,5,7-hexchloronaphthalene
1,2,3,4,5,8-hexchloronaphthalene
1,2,3,4,6,7-hexchloronaphthalene
1,2,3,5,6,7-hexchloronaphthalene
1,2,3,5,6,8-hexachloronaphthalene
1,2,3,5,7,8-hexachloronaphthalene
1,2,3,6,7,8-hexachloronaphthalene
1,2,4,5,6,8-hexachloronaphthalene
1,2,4,5,7,8-hexachloronaphthalene
1,2,3,4,5,6,7-heptachloronaphthalene
1,2,3,4,5,6,8-heptachloronaphthalene
1,2,3,4,5,6,7,8-octachloronaphthalene
In vitroa,b
3.9 × 10−07TS
3.9 × 10−07VS
6.3 × 10−04TS
2.9 × 10−04VS
4.4 × 10−04TS
2.1 × 10−03TS
4.6 × 10−04TS
In silicoc
H4IIE-luc
In silicod
3.5 × 10−06 1.9 × 10−07
3.9 × 10−07 6.2 × 10−08
5.0 × 10−06 1.3 × 10−06
2.3 × 10−06 2.9 × 10−07
6.3 × 10−06 1.9 × 10−06
1.1 × 10−04 2.2 × 10−05
4.4 × 10−05 1.1 × 10−04
1.6 × 10−04 1.3 × 10−05
2.1 × 10−04 6.9 × 10−04
4.9 × 10−04 1.0× 10−03
6.0 × 10−05 2.7 × 10−04
5.8 × 10−05 8.3 × 10−07
7.8 × 10−04 2.8 × 10−03
4.8 × 10−05 4.3 × 10−05
2.6 × 10−05 1.0 × 10−04
1.2 × 10−04 3.8 × 10−04
3.6 × 10−05 2.7 × 10−03∗
1.0 × 10−03 3.2× 10−02∗
In vitrob,e
2.6 × 10−05TS
3.9 × 10−03TS
1.0 × 10−03TS
1.5 × 10−04VS
5.9 × 10−04TS
1.0 × 10−03TS
1.0 × 10−07VS
1.0 × 10−07VS
In silicof
I
I
A
I
I
A
A
I
A
A
A
A
A
I
I
A
I
I
In silicoc
In silicod
7.6 × 10−05 2.6 × 10−06
6.8 × 10−06 5.2 × 10−07
1.0 × 10−04 2.8 × 10−05
6.0 × 10−05 1.3 × 10−05
3.2 × 10−05 1.5 × 10−05
2.3 × 10−04 2.2 × 10−05
3.8 × 10−04 1.0 × 10−05
2.7 × 10−04 8.9 × 10−08
8.3 × 10−04 2.9 × 10−03
1.4 × 10−03 1.7 × 10−03
4.5 × 10−04 1.1 × 10−04
4.3 × 10−04 1.5 × 10−04
8.1 × 10−04 7.1 × 10−04
3.8 × 10−04 1.6 × 10−07
2.9 × 10−04 8.9 × 10−08
9.3 × 10−04 1.8 × 10−03
5.2 × 10−04 1.0 × 10−07
3.2 × 10−03 8.7 × 10−08∗
TS
Training set.
Validation set.
∗
High uncertainty due to extrapolation outside of the model’s domain.
a
Villeneuve et al.[17]
b
Villeneuve et al.[49]
c
Falandysz and Puzyn[30]
d
This study.
e
Blankenship et al.[15]
f
Olivero-Verbel et al.[31] ; I = Inactive, A = Active.
VS
B3LYP and relatively large 6–311++G∗∗ basis set. This
functional (B3LYP) is a linear combination of exchangecorrelation energy from the Local Spin Density Approximation (LSDA), exchange energy difference between
Hartree Fock and LSDA, Becke’s exchange energy with
gradient correction (1988) and correlation energy with a
Lee-Young-Parr correction. The Pople style basis set 6–
311++G∗∗ is a triple split valence basis, where the core
orbitals are a contraction of six primitive Gaussian-type
functions (PGTOs). The valence split into three functions,
represented by three, one, and one PGTOs, respectively. To
develop better descriptions of the systems, diffuse and polarization functions were added for hydrogen, carbon and
chlorine atoms.[36] In such studies the 6–311++G∗∗ basis set
was found to be the optimal solution, due to both relative
high accuracy and low computation time.
The following quantum-chemical and thermo-dynamical
descriptors were used: valence angle between C1 and C8
(CCC(1–8), valence angle between C4 and C5 (CCC(4–
5), dipole moment (D), mean polarizability (A), maximal
positive and negative partial Mulliken’s charge (MaxQ+
and MaxQ-), energy of the highest occupied molecular orbital (HOMO), energy of the lowest unoccupied molecular orbital (LUMO), molecular hardness (Hard), ionization potential (IP), electron affinity (EA), total energy of
the molecule (Et), standard enthalpy of formation (dH),
standard Gibbs free energy of formation (dG), heat capacity (Cv), entropy (S), molecular refraction (MR), molar
volume (MR), solvent accessible molecular surface area in
water (SASw), solvent accessible molecular volume in water (SAVw), total electrostatic energy of solvatation in water
(TEESolw), polarized solute – solvent interaction energy in
water (PolSSw), cavitation energy in water (CEw), dispersion energy in water (DEw), total non-electrostatic energy
of solvatation in water (TNEw), solvent accessible molecular surface area in octanol (SASo), solvent accessible molecular volume in octanol (SAVo), total electrostatic energy of
solvatation in octanol (TEESolo), polarized solute-solvent
interaction energy in octanol (PolSSo), cavitation energy
in octanol (CEo), dispersion energy in octanol (DEo),
total non-electrostatic energy of solvatation in octanol
(TNEo).
Because octanol is not a standard solvent included in the
Gaussian 03 package, we characterized it using the dielectric constant εoct = 10.3 and solvent radius r = 3.250 Å.
Mean polarizability was calculated as the mean eigenvalue
from diagonalization of the polarizability tensor. Ionization
potential was determined as the difference between total
energy of fully optimized molecular cation and the neutral molecule. The electron affinity used in this study was
577
The prediction of relative potencies for chloronaphthalenes
Table 2. The list of the descriptors used∗ .
No.
Symbol
Description
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.
26
27.
28.
29.
30.
31.
32.
33.
34.
35.
36.
37.
38.
39.
40.
nCla
nClalphaa
nClbetaa
nClp1a
nClp2a
CCC(1–8)b
CCC(4–5)b
Db
Ab
MaxQ+b
MaxQ-b
HOMOb
LUMOb
Hardb
CHBBb
CHBAb
IPb
EAb
Etb
dHb
dGb
Cvb
Sb
MR
MVolb
SASwc
SAVwc
TEESolwc
PolSSwc
CEwc
DEwc
TNEwc
SASod
SAVod
TEESolod
PolSSod
CEod
DEod
TNEod
T(Cl-Cl)a
Total number of Cl atoms
Number of Cl atoms in alpha positions
Number of Cl atoms in beta positions
Number of Cl atoms present in the first aromatic ring
Number of Cl atoms present in the second aromatic ring
Valence angle between C1 and C8
Valence angle between C4 and C5
Dipole moment
Mean polarizability calculated from elements αxx , αyy , i αzz of diagonalized tensor
Maximal positive Mülliken charge
Maximal negative Mülliken charge
Energy of the highest occupied molecular orbital
Energy of the lowest unoccupied molecular orbital
Molecular hardness
Hydrogen bonding basicity
Hydrogen bonding acidity
Ionization potential
Electron affinity
Total energy of the molecule
Standard enthalpy of formation
Gibbs free energy of formation
Heat capacity (v = const.)
Entropy
Molecular refraction
Molar volume
Solvent accessible molecular surface area in water
Solvent accessible molecular volume in water
Total electrostatic energy of solvatation in water
Polarized solute – solvent interaction energy in water
Energy of cavitation in water
Dispersion energy in water
Total non-electrostatic energy of solvatation in water
Solvent accessible molecular surface area in octanol
Solvent accessible molecular volume in octanol
Total electrostatic energy of solvatation in octanol
Polarized solute – solwent interaction energy in octanol
Energy of cavitation in octanol
Dispersion energy in octanol
Solvent accessible molecular volume in octanol
Sum of topological distances between Cl..Cl
Unit
—
—
—
—
—
Degree
Degree
Debye
Å3
—
—
Hartree
Hartree
Hartree
Hartree × 10−3
Hartree × 10−3
eV
eV
Hartree
kJ mol−1
kJ mol−1
kJ mol−1
J mol−1 K−1
Å3
Å2
Å3
Hartree
kJ mol−1
kJ mol−1
kJ mol−1
kJ mol−1
Å2
Å3
Hartree
kJ mol−1
kJ mol−1
kJ mol−1
kJ mol−1
—
Topological descriptor; b quantum-chemical descriptor calculated in vaccuo (B3LYP/6–311++G∗∗ ); c quantum-chemical descriptor calculated in
water (PCM model, B3LYP/6–311++G∗∗ ); d quantum-chemical descriptor calculated in octanol (PCM model, B3LYP/6–311++G; dielectric
constant εoct = 10.3, solvent radius r = 3.250 Å); ∗ A data matrix presenting values of 40 molecular descriptors calculated for the 75 possible CN
congeners is available from the corresponding author.
a
calculated as the difference between the energy of molecular
anion and the corresponding neutral molecule. Thermodynamic descriptors were calculated based on frequency analysis using the algorithm proposed by Ochterski.[37] Topological descriptors, calculated using DRAGON software
included: total number of Cl atoms (nCl), number of Cl
atoms in alpha and beta positions (nClalpha and nClbeta),
number of chlorine atoms in the first and the second aromatic ring (nClp1 and nClp2), hydrogen-bonding basicity
and acidity (CHBB and CHBA), sum of topological distances between chlorine atoms (T(Cl-Cl).[38,39]
In the second phase of the study, autoscaling was used
to make the contribution of each of the 40 variables equal
in the final model. Internal correlations between descriptors and class homogeneity were investigated by use of an
inter-correlation matrix and principal component analysis
(PCA), which is a standard chemometrical tool used to reduce redundancy of the correlated parameters.[40−42]
578
During the third phase, predictive relationships between
the structure represented by molecular descriptors and the
REP values determined in the H4IIE and H4IIE-luc assays
were investigated. The predictive relationships were developed from the data in the training set and the predictive
power was assessed by use of the data in the validation set.
The predictive relationships were based on artificial neural
network (ANN) models, followed by optimization of the
number and composition of input variables. The optimization was carried out by use of a genetic algorithm (GA). Because the both mathematical procedures are complicated,
here we present only simplified descriptions.
The artificial neural network (ANN) technique is based
on a mathematical imitation of the functioning of the mammalian nervous system. Each of the artificial neurons is a
summation of the weighted input signals. The neuron processes the information using the transformation function
and results in a final signal that is transferred to the other
neural cells. In this way, signals are transferred and processed though the net and artificial neural networks are
able to model even very complicated and non-linear phenomena. Before predictions begin, the neural network must
first be “trained”. During training, signal weights connecting to individual input signals are matched. Because of the
issue of “overfitting,” the networks developed in our study
were trained using only data in the data set designated as
the training set. Simultaneously, we monitored the error of
prediction by use of the empirical results in the validation
data set. The training process was continued if both the
error of prediction in the based on the training set and validation set was decreasing. The process was stopped when
the error in the validation set increased significantly. Neural
networks used in this research were trained by use of two
supervised learning techniques the back-propagation (BP)
and coupled gradient algorithm (CG).[43,44]
The second artificial intelligence technique, a genetic algorithm, solves optimization problems by use of an evolutionary process resulting in a best (fittest) solution (survivor). The mathematical strategy is based on the principles
of Darwinian evolution theory. The algorithm starts with
an initial “population” that represents a set of possible solutions given by numerically expressed “chromosomes.” In
the case of variable selection, each “chromosome” is assigned a string of 0 and 1 values that indicates if an independent variable is included in the model or not. The
first set of “chromosomes” (first “population”) is selected
randomly. Solutions from the first “population” are recombined with each other, and the result of this “crossing-over”
creates a new “population.” Solutions from the new “population” characterized by the best fitness, according to the
“swindling roulette rulel,” are more likely (have a greater
probability) to reproduce. From time to time a “mutation”
operator is included, numerically by exchanging of 0 to 1
at randomly selected “chromosomes” in the “population”.
This procedure was repeated until one of the conditions
was met: (i) after finite number of iteration or (ii) until the
Puzyn et al.
number of the same chromosomes in the population exceeded a threshold of 60%. Controlling parameters of the
algorithm were set as follows: the number of chromosomes
in each generation was 100; the maximal number of generations was 100; crossing-over coefficient was 0.3; mutation
coefficient was set as 1. The neural networks had been training by means of the back-propagation (BP) method during
the first 50 epochs. After them, the learning process was
continued using coupled gradient algorithm (CG).[45]
The error of predictions in the training and validation sets
were expressed as RMSEt (root mean square error of training) and RMSEv (root mean square error of validation),
respectively. The values of both errors were calculated from
Equation 1.
n
2
i=1 (yi − yi )
(1)
RMSE =
n
where: yi —ith estimated value of the dependent variable
(REP); yi —ith observed (empirically measured) value of
the dependent variable; n-the number of compounds in the
training or validation set, respectively.
REP values based on the H4IIE and H4IIE-luc assays
were estimated for each of 75 CNs, including the congeners for which no REP values were available. The predicted results were then compared not only to empirical
results, but also to results previously predicted with other
QSAR models. Based on the sets of descriptors selected
by the GA, we also inferred potential mechanisms of CNAhR binding, and determined that this is the key determinant of the relative potencies of CN acting through the
AhR-mediated mode of toxic action. Additionally, the firstever toxic equivalency factors (TEFs) were proposed for all
congeners.
Results and discussion
Molecular descriptors
This study confirmed applicability of quantum-chemical
descriptors calculated at the level of B3LYP/6–311++G∗∗
in such QSAR studies dedicated on a set of structurally
similar compounds (congeners), like chloronaphthalenes.
It is because, in case of each descriptor (i.e., dipole moment), the standard deviation of its values calculated for
all 75 congeners were always about 3 times greater than the
absolute error of calculation (i.e. dipole moment) by the
B3LYP/6311++G∗∗ method. In other words, application
of this quantum-mechanical method resulted in descriptors
very accurate discriminating relatively small differences in
values of the descriptor (i.e., dipole moment) between congeners. In effect, REP values could be effectively predicted
from the molecular descriptor applied. A data matrix presenting values of 40 molecular descriptors calculated for
the 75 possible CN congeners is shown in the Appendix.
The prediction of relative potencies for chloronaphthalenes
579
Fig. 1. A projection of the molecular feature space on the plane restricted by the first (VW1) and the third (VW3) rotated factor.
Principal component analysis (PCA) used for multidimensional visualization of these data confirmed their homogeneity. In the linear map, which is a projection of the
molecular feature space on the plane restricted by the first
and the third rotated factor (after VARIMAX rotation)
(Fig. 1), those CNs, which are the most toxic in vivo and
had the greatest REP values based on the H4IIE and H4IIEluc assays are grouped in the top-right corner on the plot.
The first varivector (x-axis, V1), followed by the loading
values of individual descriptors (data not shown), can be
interpret as the size of a molecule. The value of V1 distinguished the homologue groups of chloronaphthalenes.
The third factor (y-axis, V3), which is influenced mainly
by the ionization potential and presence of chlorine atoms
in beta positions, separated CNs inside individual homologue groups. This result suggests that ionization potential
as much as the number of chlorine atoms in beta positions
seems to be an appropriate molecular parameter to predict REP values of CNs acting through an AhR-mediated
mechanism of action.
An additional important parameter characterizing the network was the quotient of the standard deviation of the residuals (se ) and responses of the model (sy ). The values of the
quotient se /sy were 0.17 and 0.14 for the training and validation sets, respectively. Because of the fact that these values
are near 0, the network characterizes by good quality and
explains a significant part of the information in the data set.
A strong correlation was observed between the emperical
(measured in vitro) and predicted REP values of CNs as
determined in the H4IIE bioassay (Fig. 3). The correlation
coefficient “r” for the training set was 0.985, while that of
Predicted REP values based on the H4IIE assay
The three-layer architecture of the best network chosen
from the final generation is presented (Fig. 2). The model
is characterized by relatively low values of the root mean
square errors of prediction in the training and validation
sets (RMSEt = 0.253 and RMSEv = 0.267), respectively.
Fig. 2. Architecture of the artificial neural network used for prediction of REP values based on the H4IIE (EROD) assay.
580
Puzyn et al.
Fig. 3. A plot of predicted vs. observed (experimental) REP values based on the H4IIE (EROD) assay.
the validation set was 0.991. These observations confirm
that the predictive relationships developed to predict REP
values from the H4IIE bioassay were accurate.
Predicted REP values based on the H4IIE-luc assay
The artificial neural network selected to predict REP values based on the H4IIE-luc assay (Fig. 4) were characterized by RMSEt = 0.230 and RMSEv = 0.180. Similarly,
values of the quotient se /sy were 0.14 for both, the train-
ing and the validation set. In this case, there was also high
correlation observed between in silico and in vitro results.
The correlation coefficients were rt = 0.990 for the training
and rv = 0.990 for the validation set, respectively. The plot
of observed vs. predicted values of the response in the luciferase bioassay is presented (Fig. 5). All of these features
qualified the network as the predictive relationship able to
accurately estimate REP values based on H4II-luc assay for
chloronaphthalenes.
Correlation between predicted REP values
The relationship between the predicted REP data sets was
assessed by plotting the predicted REP values for each congener (Fig. 6). Congeners 65, 71, 72, 74 and 75 were not included in this analysis since their predicted values for REP
H4IIE-luc did not appear to be accurately predicted by the
model. When the predicted REP values for the different
congeners were compared, the general trends in the REP
with increasing congener number were similar for the two
assay systems indicating that both systems, are able to accurately predict the relative potency of the different congeners. However, in general the potencies predicted using
the H4IIE data set were lower, with higher REP values, than
those predicted by the H4IIE-luc data set.
Development of TEF values
Fig. 4. Architecture of the artificial neural network used for prediction of REP values based on the H4IIE-luc assay.
For use in risk assessment of chemicals active at the Ahreceptor the TEF approach has proven very effective. The
The prediction of relative potencies for chloronaphthalenes
581
Fig. 5. A plot of predicted vs. observed (experimental) REP values based on the H4IIE-luc assay.
TEF value for each chemical relates its biological potency
to that of the most potent agonist of the receptor, 2,3,7,8TCDD. To date TEF values have been defined for the
most active chemicals PCDDs PCDFS and PCBs.[18] Development of TEFs for other compounds will allow for
assessment of the relative toxicological contributions of
each compound of class to the overall toxicity of chemical mixtures.
To develop TEF values for CNs we selected the highest predicted REP value for each compound and expressed
that potency relative to 2,3,7,8-TCDD. The TEF values
were then rounded to the next highest order of magnitude
to simplify the TEF (Table 3). While TEF values derived
from the two assay systems were in general agreement in
some cases TEFs were different by greater than an order of
magnitude. To ensure the protective nature of the TEFs the
largest of the two TEFs was selected in these cases.
The REP values determined for the CNs, either by in vitro
bioassay or the predicted values of the CNs were comparable to REP values reported by other researchers and also
for other compounds such as non- and mono-ortho chlorobiphenyls, which express AhR-mediated activity.[15,17,30,31]
The earlier predicted REP values were developed by use of
Table 3. Proposed TEF values, telative to 2,3,7,8-TCDD for PCN
congeners.
Congeners
Fig. 6. Comparison of Predicted REP values for different PCN
congeners. H4IIE = filled squares and dashed line, H4IIE-luc =
empty squares and solid line. Lines are linear best-fit.
75∗
66 70 74∗ 73 67
68 48∗ 69∗ 64∗ 72∗
55 56 54 71∗ 50 52 60∗ 41 63 31∗ 51 62 40∗ 45 61∗ 65∗
18∗ 53∗ 29 38∗ 35∗ 22∗ 42 30 36 58∗ 27 33∗ 44 9∗ 23∗ 57
37 34 21∗
26 13∗ 28∗ 49 43∗ 39 11∗ 17 32∗ 59 12 14∗ 15 16 10∗
47∗ 6∗ 19∗ 3 20 24 2∗ 5∗ 4
1 8 7 25 46
∗
TEF
0.1
0.01
0.001
0.0001
0.00001
0.000001
1 × 10−07
Indicates greater than 1 order of magnitude of uncertainty between assay
systems. The greater of the two values was used for the TEF.
582
principal component regression or discriminant analysis.
The predictive models used quantum-chemical descriptors
calculated at the semi-empirical PM3 or B3LYP density
functional level with 6–31G* basis set. In the earlier studies
CN congeners exhibiting measurable REP values were nos.
47, 50, 51, 52, 54, 60, 63, 64, 66, 67, 68, 69, 70 and 74 or
48, 54, 56, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74 and
75. When the GA-ANN model was applied, two additional
CNs, nos. 55 and 62, were identified.[30,31] The GA-ANN
model used in those studies utilizes molecular descriptors
calculated at the highest level of quantum-mechanical theory and are based on a larger data set, than other QSAR
approaches. Both of the models that were applied previously were cross-validated but not externally validated.[30,31]
Therefore the predictions made by use of the GA-ANN
model are considered less reliable. The differences between
the observed and predicted REP values were much less in
the current study than in previous predictions of REP values for CNs (Table 1). This observation confirms that implementation of the GA-ANN technique is useful for predicting REPs for AhR-ligands.
Based on the battery of descriptors selected by GA that
were used in the final predictive relationships some generalities about the more predictive molecular properties of REP
values of CNs can be made. These seem to be three primary
classes of descriptors that were useful in predicting REP
values. One was size and volume of a molecule. The second (represented by CCC(4-5) is related to the planarity
of a molecule, while the third group represents descriptors related to the substitution pattern of chlorine atoms.
Those congeners, which have more chlorine atoms in β positions (2,3,6 and 7), which are characterized with the greatest ionization potentials, exhibit the greatest REP values.
Substitution pattern of chlorine atoms also determines the
distribution of the partial Mulliken charges. It appears for
this analysis that interactions of chloronaphthalenes with
the AhR are affected by the following three primary factors: size of the molecule, steric interactions and, electrostatic interactions, which seems to be the most important
parameter.
In our studies REP values determined by use of the two
assays were similar. This observation is similar to the results of other studies that have observed strong correlations between REP values from the H4IIE and H4IIE-luc
assays.[46] However, there were some differences between the
REP values determined for individual congeners by use of
the H4IIE and H4IIE-luc assays. The H4IIE assay, which
uses changes in expression of the endogenous reporter gene
(CYP-1 A) that codes for EROD activity is standard and
one of the most used assays to determine the potency of
individual AhR-active compounds and mixtures.[20] While
the REP values based on the H4IIE and H4IIE-luc assays, were similar for CNs they can be different for other
compounds, such as PCBs, can inhibit 7-ethoxyresorufin
O-deethylase, which leads to lesser induction.[46,47] Also,
Puzyn et al.
the H4IIE assay is sensitive to oxidative stress and results
are dependent on the species or cell type [47]. Since the
H4IIE-luc assay is not based on EROD activity it does not
have these limitations. It is faster and not as sensitive to
inhibition.
The relatively great REP values predicted for CNs
nos. 74 and 75 based on the results of the H4IIE assay
are not in agreement with other observations. Although,
these congeners have been reported to be toxic, based
on our understanding of the molecular descriptors that
predict REP values these compounds should not exhibit
such great REP values of congeners nos. 66, 67, 70 and
73.[48] The most probable reason of this likely artefact is
extrapolation (prediction outside of the predictive relationship’s domain). This extrapolation was necessary due
to the lack of experimental data for the more chlorinated
chloronaphthalene congeners.
TCDD Equivalency Factors (TEFs) are consensus values
derived from studies of several species and or end-points.
Collection of this information for the complete set of 75
possible CNs for which no in vivo or in vitro information is
currently available would be time-consuming and costly. Estimated REPs values, such as those reported here were used
to develop preliminary TEFs for chloronaphthalenes. Although it is generally preferable to develop TEFs based on a
variety of in vitro and in vivo endpoints the TEFs presented
here represent a first approximation of values for CNs.[18]
These TEFs are of particular relevance for the comparison of toxicological contributions from the different CN
congeners and the additive toxicity of different CN mixtures. In addition, during development of the TEFs values
were rounded to the next highest order of magnitude making the TEFs protective rather than predictive. The TEFs
developed ranged to values as low as 1 × 10−7 , which is
considerably lower than values applied to PCDD/Fs and
PCBs. While such low TEF values may seem toxicologically irrelevant it needs to be remembered that PCNs have
the potential to occur at environmental concentrations several orders of magnitude greater than PCDD/Fs. Therefore
even PCNs with relatively low TEFs may be toxicologically
relevant at environmental concentrations when compared
to PCDD/Fs. Even so it may simply assessment somewhat
to group all congeners together that have a TEF of 1 × 10−6
or less and give them a TEF of 1 × 10−6 , thereby equating
ppt of PCDD/F to ppm of PCNs.
Acknowledgments
This study was supported by the Ministry of Education
and Science under Grant no. KBN 1128/T09/2003/24 and
DS/8250-4-0092-6. Computations were conducted using
computers in the Academic Computer Center in Gdańsk
TASK. Dr. Tomasz Puzyn is the recipient of a fellowship
from the Foundation for the Polish Science.
The prediction of relative potencies for chloronaphthalenes
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5272–5297.
Hilscherova, K.; Machala, M.; Kannan, K.; Blankenship, A.L.;
Giesy, J.P. Cell bioassays for detection of aryl hydrocarbon (AhR)
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585
1
nCl
1
1
2
2
2
2
2
2
2
2
2
2
3
3
3
3
3
3
3
3
3
3
3
3
3
3
4
4
4
4
4
4
4
4
4
4
4
#CN
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
1
1
2
2
1
1
2
3
2
1
0
2
2
1
1
2
3
2
2
3
2
2
1
0
1
1
2
2
1
1
2
0
0
0
1
2
2
2
nCl alpha
2
2
1
1
2
2
1
0
1
2
3
2
2
3
3
2
1
2
2
1
2
2
0
1
1
1
0
0
1
1
0
2
2
2
2
1
1
3
nC lbeta
2
2
2
2
2
2
2
2
2
1
2
4
3
3
3
3
3
3
3
3
2
2
1
1
2
2
2
1
1
1
1
2
1
1
3
3
2
4
nClp1
1
1
1
1
1
1
1
1
1
2
1
0
1
1
1
1
1
1
1
1
2
2
0
0
0
0
0
1
1
1
1
0
1
1
0
0
1
5
nClp2
123.27
122.81
128.30
123.22
124.00
123.53
128.86
128.00
123.27
123.68
121.74
122.56
122.80
123.54
123.06
128.58
119.67
122.50
122.03
127.43
122.46
122.05
124.00
121.91
123.24
123.99
123.21
123.22
124.00
124.00
128.90
122.11
121.99
121.52
123.50
122.46
122.51
6
CCC(1–8)
APPENDIX. Values of molecular descriptors nos. 1–15.
121.14
121.62
118.29
122.74
120.75
121.19
118.05
120.36
122.80
121.44
122.18
122.56
122.76
120.79
121.27
117.90
127.86
122.72
123.17
120.13
122.46
123.18
121.55
122.41
121.52
121.01
123.21
123.22
121.16
121.64
118.47
122.13
121.99
122.47
121.14
123.15
123.16
7
CCC(4–5)
1.23
2.97
3.79
1.87
0.35
1.59
2.70
1.49
1.32
3.01
1.48
2.88
2.54
1.22
2.32
3.50
1.86
0.48
1.37
2.45
0.00
1.35
1.79
2.04
2.85
2.20
0.69
0.00
1.59
2.91
3.21
3.01
0.00
1.80
3.20
2.12
1.49
8
D
23.83
23.76
23.32
23.52
23.98
23.94
23.52
23.23
23.56
23.71
24.22
25.50
25.62
26.13
26.08
25.59
25.50
25.90
25.89
25.46
25.74
25.85
19.19
19.55
21.36
21.52
21.19
21.17
21.54
21.48
21.12
21.71
21.95
21.94
23.57
23.41
23.43
9
A
1.48
1.78
1.85
1.07
0.62
0.74
1.74
1.44
1.44
1.30
1.00
0.69
0.70
0.80
0.85
1.04
1.63
0.65
1.10
1.02
1.01
1.42
1.61
0.87
1.77
1.38
1.14
0.88
1.40
1.60
1.94
0.75
0.66
0.73
1.06
1.19
1.26
10
MaxQ+
12
HOMO
−0.230990
−0.233570
−0.236840
−0.238050
−0.235140
−0.236050
−0.238410
−0.237620
−0.230900
−0.238840
−0.239280
−0.241720
−0.241400
−0.240640
−0.241580
−0.242840
−0.243690
−0.236060
−0.242120
−0.245690
−0.243150
−0.238030
−0.235450
−0.241540
−0.242620
−0.245160
−0.243730
−0.245270
−0.247880
−0.246840
−0.240520
−0.240890
−0.245980
−0.247150
−0.240270
−0.246150
−0.247790
11
MaxQ−
−1.58
−0.92
−1.27
−1.67
−1.44
−1.03
−2.05
−1.73
−1.03
−1.10
−0.76
−0.93
−1.35
−1.47
−1.41
−1.67
−1.79
−1.00
−1.83
−2.10
−1.57
−1.24
−1.27
−1.90
−2.53
−1.39
−0.92
−2.03
−1.63
−1.87
−1.21
−1.24
−1.77
−1.81
−1.39
−1.43
−1.96
0.16022
0.16047
0.15620
0.16126
0.16267
0.16145
0.15853
0.15777
0.16106
0.16062
0.16157
0.16327
0.16524
0.16623
0.16564
0.16200
0.16326
0.16611
0.16658
0.16279
0.16521
0.16694
0.14651
0.14741
0.15274
0.15454
0.15356
0.15408
0.15487
0.15439
0.15071
0.15403
0.15495
0.15605
0.15881
0.15928
0.15993
14
Hard
0.29734
0.29733
0.29741
0.29735
0.29731
0.29734
0.29739
0.29741
0.29735
0.29734
0.29732
0.29733
0.29731
0.29729
0.29730
0.29736
0.29736
0.29731
0.29730
0.29736
0.29731
0.29729
0.29746
0.29743
0.29740
0.29739
0.29742
0.29741
0.29738
0.29739
0.29746
0.29738
0.29737
0.29735
0.29735
0.29736
0.29735
15
CHBB
(Continued on next page)
0.077590
0.077240
0.076330
0.080390
0.079640
0.079740
0.079020
0.080080
0.080580
0.078620
0.077970
0.082810
0.085210
0.084580
0.084440
0.083480
0.085630
0.086230
0.086000
0.085300
0.084270
0.086090
0.062030
0.061250
0.068630
0.071030
0.071980
0.072100
0.071320
0.071160
0.070520
0.069220
0.070610
0.070370
0.076210
0.077920
0.078280
13
LUMO
586
1
nCl
4
4
4
4
4
4
4
4
4
4
4
5
5
5
5
5
5
5
5
5
5
5
5
5
5
6
6
6
6
6
6
6
6
6
6
7
7
8
#CN
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
3
1
2
2
2
3
1
2
4
2
0
3
2
2
2
3
1
2
2
3
3
4
2
3
3
3
3
4
2
2
3
3
3
4
4
3
4
4
2
nCl alpha
1
3
2
2
2
1
3
2
0
2
4
2
3
3
3
2
4
3
3
2
2
1
3
2
2
3
3
2
4
4
2
3
4
2
2
4
3
4
3
nC lbeta
2
2
2
2
2
2
2
2
2
2
2
4
4
3
3
3
3
3
3
3
3
3
3
3
3
4
4
4
4
3
3
3
3
3
3
4
4
4
4
nClp1
2
2
2
2
2
2
2
2
2
2
2
1
1
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
3
3
3
3
3
3
3
3
4
5
nClp2
127.46
122.94
128.29
127.83
122.72
127.99
123.72
128.80
125.26
122.99
121.98
119.56
122.61
122.75
122.33
127.74
123.24
128.55
128.11
119.48
119.23
124.71
122.27
127.41
126.93
119.34
119.14
124.66
122.37
122.36
127.64
127.26
128.39
124.67
124.22
118.93
124.39
124.14
6
CCC(1–8)
APPENDIX. Values of molecular descriptors nos. 1–15. (Continued)
120.13
121.41
117.85
118.05
122.74
119.89
120.98
117.66
125.26
122.99
121.98
127.56
122.18
122.09
122.81
119.74
121.04
117.49
117.66
127.31
127.87
125.01
122.92
119.68
119.90
127.09
127.56
124.66
122.37
122.36
119.09
119.48
117.30
124.46
124.75
127.41
124.20
124.14
7
CCC(4–5)
2.09
2.30
2.49
3.81
0.00
1.38
1.07
1.16
0.00
2.13
0.00
2.70
1.41
1.38
0.97
2.25
1.10
1.71
2.99
1.30
0.03
1.32
0.92
0.93
2.19
1.92
0.87
1.92
0.03
0.00
0.84
1.49
1.84
0.00
0.82
0.76
0.77
0.00
8
D
24.46
26.09
25.83
25.60
25.99
25.64
26.27
26.02
25.42
25.80
26.61
27.58
28.08
28.04
28.19
27.75
28.52
28.19
28.00
27.85
27.99
27.70
28.23
27.98
27.79
30.02
30.19
29.83
30.49
30.47
30.20
30.19
30.44
30.06
30.03
32.47
32.24
34.52
9
A
1.11
1.09
1.47
1.86
0.56
1.73
0.70
0.66
0.74
0.96
0.94
0.77
0.68
1.00
0.78
0.74
0.93
0.74
1.46
0.82
1.41
1.18
0.65
0.74
1.03
0.74
0.76
0.78
0.75
1.16
0.97
0.76
0.75
0.75
0.75
0.77
0.78
0.76
10
MaxQ+
12
HOMO
−0.240320
−0.247540
−0.242350
−0.241250
−0.247070
−0.241690
−0.248500
−0.245150
−0.235380
−0.245630
−0.249720
−0.243230
−0.249320
−0.250230
−0.250360
−0.244060
−0.251430
−0.246940
−0.245010
−0.245170
−0.247090
−0.240170
−0.250380
−0.245790
−0.245080
−0.247620
−0.248880
−0.242470
−0.253020
−0.253300
−0.249060
−0.248280
−0.249000
−0.244630
−0.244890
−0.250970
−0.246870
−0.248890
11
MaxQ−
−1.49
−2.32
−1.37
−1.07
−1.29
−2.23
−2.36
−1.11
−0.95
−2.58
−1.51
−1.24
−1.30
−2.11
−2.00
−2.64
−2.47
−1.38
−1.49
−1.39
−1.86
−1.48
−2.46
−2.19
−1.69
−1.59
−1.34
−1.15
−1.90
−2.17
−1.97
−3.07
−1.38
−1.23
−1.06
−1.79
−0.97
−0.75
0.085510
0.084410
0.084580
0.081690
0.088390
0.087890
0.086600
0.086830
0.087920
0.087420
0.085040
0.089930
0.090710
0.090790
0.092730
0.092060
0.091150
0.091150
0.088630
0.090940
0.093000
0.092770
0.092670
0.092900
0.090310
0.094840
0.097140
0.096780
0.096980
0.097050
0.097100
0.096680
0.095090
0.097550
0.097220
0.100870
0.101190
0.104810
13
LUMO
0.16292
0.16598
0.16347
0.16147
0.16773
0.16479
0.16755
0.16599
0.16165
0.16653
0.16738
0.16658
0.17002
0.17051
0.17155
0.16806
0.17129
0.16905
0.16682
0.16806
0.17005
0.16647
0.17153
0.16935
0.16770
0.17123
0.17301
0.16963
0.17500
0.17518
0.17308
0.17248
0.17205
0.17109
0.17106
0.17592
0.17403
0.17685
14
Hard
0.29736
0.29729
0.29734
0.29735
0.29730
0.29735
0.29728
0.29732
0.29741
0.29731
0.29727
0.29733
0.29727
0.29726
0.29726
0.29733
0.29725
0.29730
0.29732
0.29732
0.29730
0.29737
0.29726
0.29731
0.29732
0.29729
0.29728
0.29734
0.29724
0.29723
0.29728
0.29728
0.29728
0.29732
0.29732
0.29726
0.29730
0.29728
15
CHBB
587
16
CHBA
0.29615
0.29616
0.29608
0.29606
0.29605
0.29605
0.29606
0.29606
0.29607
0.29608
0.29606
0.29607
0.29601
0.29599
0.29599
0.29599
0.29600
0.29601
0.29597
0.29597
0.29597
0.29598
0.29597
0.29596
0.29598
0.29599
0.29594
0.29592
0.29592
0.29593
0.29594
0.29591
0.29591
0.29591
0.29592
0.29593
0.29591
#CN
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
7.9243
7.9872
8.0102
8.0342
7.9547
7.9889
8.0464
8.0203
7.8608
8.0837
8.0438
8.1355
8.0881
8.0421
8.0774
8.0915
8.1274
7.9367
8.0810
8.1767
8.0889
7.9866
7.9147
8.0590
8.1152
8.1689
8.0907
8.1344
8.1939
8.1610
8.0165
8.0106
8.1189
8.1591
7.9888
8.1389
8.1840
17
IP
19
Et
381.56
381.10
359.67
359.60
359.97
359.89
359.67
359.52
359.33
359.17
359.17
359.29
337.55
338.00
337.83
337.59
337.50
336.87
337.73
337.53
337.38
337.17
337.37
337.85
337.57
337.25
315.56
315.52
315.46
315.36
314.62
315.30
315.62
315.79
312.45
315.85
315.67
18
EA
−0.1431
−0.1235
−0.3647
−0.4401
−0.4624
−0.4665
−0.4468
−0.4429
−0.4213
−0.3865
−0.4310
−0.4230
−0.6184
−0.6650
−0.6744
−0.6586
−0.6480
−0.6213
−0.7410
−0.7222
−0.7278
−0.7021
−0.7270
−0.7450
−0.6898
−0.6739
−0.8370
−0.9080
−0.8935
−0.8901
−0.8612
−0.9184
−0.9392
−0.9321
−0.9114
−0.8775
−0.9345
81.32
77.64
−17.41
−28.14
−24.78
−25.38
−30.47
−29.94
9.90
−20.15
−33.89
−33.94
−111.81
−120.75
−123.47
−128.25
−127.98
−84.33
−133.44
−138.82
−138.13
−99.01
−92.19
−134.98
−127.11
−130.85
−200.97
−216.74
−221.87
−221.57
−177.67
−187.71
−230.12
−230.47
−185.90
−220.72
−230.78
20
dH
APPENDIX. Values of molecular descriptors nos. 16–30.
2849.53
2845.56
2454.45
2443.35
2448.73
2448.10
2441.02
2441.54
2479.94
2451.37
2439.00
2439.00
2063.70
2054.66
2051.86
2046.82
2047.09
2087.89
2041.56
2035.94
2036.56
2074.31
2080.59
2040.05
2047.99
2043.97
1680.13
1662.22
1656.88
1657.21
1697.17
1688.66
1648.41
1648.18
1698.70
1658.24
1647.87
21
dG
136.76
137.18
152.57
152.91
152.59
152.75
152.79
152.89
152.62
152.93
153.24
153.11
168.49
168.47
168.63
168.76
168.75
168.81
168.91
169.04
169.22
168.88
168.79
168.72
168.60
168.93
184.42
184.67
184.61
184.63
184.78
184.69
184.88
184.60
176.71
184.42
184.79
22
Cv
372.32
373.33
401.00
402.21
395.42
395.56
402.21
402.25
407.07
402.12
397.54
397.37
429.62
429.95
430.24
431.09
431.10
440.65
431.34
432.13
432.38
436.98
438.78
431.26
430.99
431.93
451.76
458.92
459.67
459.57
472.80
467.62
460.39
459.98
440.02
458.94
460.01
23
S
47.313
47.313
52.118
52.118
52.118
52.118
52.118
52.118
52.118
52.118
52.118
52.118
56.923
56.923
56.923
56.923
56.923
56.923
56.923
56.923
56.923
56.923
56.923
56.923
56.923
56.923
61.727
61.727
61.727
61.727
61.727
61.727
61.727
61.727
61.727
61.727
61.727
24
MR
131.039
153.891
137.924
144.725
119.866
132.495
120.462
112.490
122.045
134.108
139.086
137.442
148.898
114.686
133.670
113.741
137.738
161.284
137.697
145.720
123.159
139.100
164.180
161.292
160.162
136.364
166.223
160.854
168.859
186.882
188.167
158.056
131.751
170.779
152.458
145.337
171.097
25
MVol
352.55
358.30
376.77
382.92
377.17
377.04
382.98
382.87
371.95
382.95
388.72
389.40
400.88
401.36
401.26
407.22
407.01
395.52
407.29
413.26
413.21
402.41
396.24
407.49
407.53
413.27
423.25
425.24
431.26
431.10
419.69
420.49
431.70
431.62
419.78
425.55
431.46
26
SASw
554.10
560.85
603.53
610.71
603.93
603.77
610.79
610.58
598.18
610.83
617.38
618.24
652.70
653.30
653.23
660.17
659.81
646.58
660.12
667.16
667.10
654.79
647.41
660.31
660.67
667.25
703.05
702.07
709.22
709.14
695.81
696.71
709.69
709.60
695.67
702.61
709.44
27
SAVw
29
PolSSw
30
CEw
−845.5634
−2.72
152.80
−845.5638
−2.76
155.23
−1305.1783
−3.22
162.46
−1305.1812
−2.22
164.89
−1305.1809
−2.05
162.46
−1305.1811
−2.13
162.42
−1305.1822
−2.38
164.93
−1305.1821
−3.05
164.89
−1305.1687
−3.97
160.54
−1305.1782
−3.22
165.10
−1305.1826
−2.64
167.36
−1305.1825
−2.72
167.65
−1764.7912
−3.05
172.13
−1764.7944
−1.97
172.13
−1764.7955
−2.13
172.09
−1764.7965
−2.51
174.64
−1764.7966
−3.18
174.51
−1764.7817
−4.27
170.00
−1764.7982
−1.63
174.47
−1764.7993
−1.55
176.98
−1764.7991
−2.05
176.98
−1764.7860
−2.93
172.72
−1764.7843
−2.55
170.08
−1764.7989
−1.76
174.56
−1764.7960
−2.89
174.77
−1764.7966
−2.80
177.19
−2224.4031
−2.34
179.16
−2224.4079
−2.09
181.71
−2224.4091
−2.09
184.26
−2224.4090
−2.59
184.18
−2224.3940
−3.64
179.66
−2224.3975
−2.09
179.79
−2224.4119
−1.26
184.26
−2224.4121
−1.30
184.22
−2224.3959
−2.34
179.54
−2224.4096
−1.88
181.84
−2224.4122
−1.34
184.14
(Continued on next page)
28
TEESolw
588
16
CHBA
0.29592
0.29593
0.29592
0.29595
0.29589
0.29589
0.29590
0.29590
0.29589
0.29590
0.29592
0.29587
0.29586
0.29586
0.29584
0.29585
0.29586
0.29586
0.29588
0.29586
0.29584
0.29584
0.29584
0.29584
0.29587
0.29582
0.29580
0.29580
0.29580
0.29580
0.29580
0.29580
0.29582
0.29579
0.29580
0.29576
0.29576
0.29572
#CN
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
7.9910
8.1847
8.0387
8.0211
8.1425
8.0221
8.2020
8.1202
7.8605
8.1317
8.2557
8.0388
8.1809
8.2154
8.2072
8.0549
8.2519
8.1295
8.0798
8.0727
8.1236
7.9438
8.2064
8.0801
8.0729
8.1089
8.1358
7.9749
8.2479
8.2595
8.1441
8.1195
8.1533
8.0169
8.0286
8.1651
8.0524
8.0795
17
IP
19
Et
312.40
315.42
314.79
312.24
315.42
315.22
315.35
315.07
309.21
315.74
315.50
290.09
293.23
293.51
293.33
290.12
293.43
292.45
289.97
290.47
293.03
286.86
293.56
290.19
290.05
267.60
267.72
264.17
271.18
271.27
268.03
267.80
267.79
264.46
264.46
245.32
241.68
218.67
18
EA
−0.9164
−0.8890
−0.8930
−0.8085
−1.0052
−0.9873
−0.9546
−0.9611
−0.9817
−0.9727
−0.9082
−1.0723
−1.0973
−1.0977
−1.1583
−1.1357
−1.1134
−1.1151
−1.0398
−1.1024
−1.1633
−1.1526
−1.1542
−1.1630
−1.0836
−1.2428
−1.3120
−1.2980
−1.3069
−1.3093
−1.3096
−1.2982
−1.2562
−1.3249
−1.3099
−1.4454
−1.4520
−1.5815
−188.01
−224.39
−192.25
−179.47
−240.33
−199.74
−234.50
−206.72
−153.04
−231.14
−226.93
−264.21
−310.07
−313.72
−323.34
−280.40
−317.40
−284.86
−272.18
−282.86
−294.54
−243.12
−326.03
−292.79
−277.96
−355.65
−370.70
−314.33
−405.43
−406.17
−374.94
−369.75
−364.26
−332.74
−331.71
−447.32
−402.14
−470.55
20
dH
1696.55
1654.31
1683.59
1705.39
1637.94
1676.40
1643.84
1669.94
1738.43
1649.28
1653.36
1324.59
1272.50
1268.89
1258.94
1308.20
1264.98
1293.54
1316.53
1305.52
1285.16
1350.68
1256.21
1295.36
1310.41
936.76
921.50
985.54
882.58
880.20
917.42
922.54
928.38
965.07
966.12
549.01
599.86
236.94
21
dG
APPENDIX. Values of molecular descriptors nos. 16–30. (Continued)
176.76
184.61
185.09
176.34
185.32
184.93
184.95
185.09
169.03
184.49
184.29
192.41
200.79
200.45
200.82
192.56
200.27
201.06
192.21
192.43
200.89
184.96
200.57
192.94
192.64
208.46
208.75
200.58
216.44
216.29
208.45
208.43
208.02
200.96
200.85
224.23
216.49
232.29
22
Cv
440.15
459.84
469.42
439.15
461.27
468.38
461.02
466.67
417.00
454.04
454.47
466.86
487.74
487.62
488.70
467.49
488.35
501.72
467.13
468.26
497.34
450.07
488.85
469.00
468.28
495.60
496.31
470.57
510.39
515.86
495.81
496.01
494.82
477.50
477.41
523.35
504.35
526.82
23
S
61.727
61.727
61.727
61.727
61.727
61.727
61.727
61.727
61.727
61.727
61.727
66.532
66.532
66.532
66.532
66.532
66.532
66.532
66.532
66.532
66.532
66.532
66.532
66.532
66.532
71.337
71.337
71.337
71.337
71.337
71.337
71.337
71.337
71.337
71.337
76.142
76.142
80.947
24
MR
141.334
143.025
171.856
135.214
133.849
165.371
145.255
137.004
180.756
140.153
146.299
154.009
175.960
197.968
151.635
169.928
175.900
162.632
180.655
138.876
190.032
152.144
173.036
187.393
165.815
182.022
176.601
208.679
165.822
187.317
215.209
194.913
208.006
180.258
182.660
185.617
175.649
203.445
25
MVol
419.86
431.70
426.04
418.92
437.55
426.58
437.82
432.79
414.99
432.12
437.88
436.92
448.91
449.50
455.44
443.93
455.76
450.13
443.08
444.14
450.77
438.48
456.29
450.13
443.14
460.32
467.23
454.73
473.56
473.48
468.19
467.21
467.19
462.06
461.74
484.35
477.97
493.82
26
SASw
695.79
709.91
703.21
694.70
716.46
703.81
717.03
711.20
690.39
710.31
717.17
736.33
750.60
751.52
758.31
744.83
759.06
752.23
743.83
745.14
753.03
738.56
759.65
752.08
743.74
784.44
792.73
777.90
800.56
800.54
794.16
792.73
792.93
786.88
786.43
833.41
825.65
864.34
27
SAVw
−2224.3968
−2224.4101
−2224.3985
−2224.3941
−2224.4146
−2224.4010
−2224.4127
−2224.4027
−2224.3829
−2224.4124
−2224.4102
−2684.0037
−2684.0203
−2684.0217
−2684.0242
−2684.0087
−2684.0223
−2684.0104
−2684.0061
−2684.0097
−2684.0138
−2683.9940
−2684.0252
−2684.0122
−2684.0079
−3143.6153
−3143.6197
−3143.5991
−3143.6333
−3143.6336
−3143.6213
−3143.6195
−3143.6178
−3143.6048
−3143.6045
−3603.2268
−3603.2092
−4062.8130
28
TEESolw
−2.43
−2.76
−2.93
−4.35
−0.63
−1.59
−1.63
−1.59
−2.34
−1.67
−2.59
−2.43
−1.42
−1.55
−0.84
−1.92
−1.97
−2.01
−3.47
−1.67
−0.75
−1.80
−0.92
−0.96
−1.97
−1.88
−0.84
−1.88
−0.84
−0.88
−0.92
−1.17
−2.34
−1.00
−1.05
−0.92
−0.96
−0.84
29
PolSSw
179.58
184.43
182.17
179.33
186.56
182.21
186.82
184.81
177.61
184.43
187.02
186.44
191.17
191.42
193.76
189.20
194.05
191.79
189.03
189.28
191.88
187.02
194.10
191.63
188.87
195.81
198.53
193.59
201.04
201.00
198.91
198.53
198.70
196.48
196.36
205.43
202.92
209.33
30
CEw
589
30
CEw
152.80
155.23
162.46
164.89
162.46
162.42
164.93
164.89
160.54
165.10
167.36
167.65
172.13
172.13
172.09
174.64
174.51
170.00
174.47
176.98
176.98
172.72
170.08
174.56
174.77
177.19
179.16
181.71
184.26
184.18
179.66
179.79
184.26
184.22
179.54
181.84
29
PolSSw
−2.72
−2.76
−3.22
−2.22
−2.05
−2.13
−2.38
−3.05
−3.97
−3.22
−2.64
−2.72
−3.05
−1.97
−2.13
−2.51
−3.18
−4.27
−1.63
−1.55
−2.05
−2.93
−2.55
−1.76
−2.89
−2.80
−2.34
−2.09
−2.09
−2.59
−3.64
−2.09
−1.26
−1.30
−2.34
−1.88
#CN
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
−26.23
−26.44
−28.70
−28.83
−28.74
−28.70
−28.91
−28.74
−28.62
−28.83
−29.12
−29.08
−31.05
−31.05
−31.13
−31.34
−31.17
−31.00
−31.13
−31.46
−31.34
−31.17
−31.05
−31.17
−31.13
−31.51
−33.81
−33.30
−33.68
−33.51
−33.35
−33.35
−33.51
−33.51
−33.35
−33.56
31
DEw
126.57
128.83
133.80
136.11
133.76
133.72
136.06
136.15
131.96
136.27
138.28
138.57
141.13
141.13
141.00
143.30
143.39
138.99
143.39
145.60
145.69
141.59
139.08
143.43
143.64
145.73
145.39
148.45
150.62
150.71
146.36
146.48
150.79
150.75
146.23
148.32
32
TNEw
APPENDIX. Values of molecular descriptors nos. 31–40.
650.30
659.81
683.61
694.05
684.89
684.35
694.16
694.00
675.46
693.75
703.64
703.58
716.77
717.87
717.69
727.51
727.26
707.88
727.94
737.79
737.72
719.39
709.29
728.29
728.01
737.49
739.54
750.72
760.57
760.36
741.15
742.68
761.61
761.47
741.61
751.11
33
SASo
1475.78
1496.58
1578.67
1601.26
1580.81
1579.76
1601.50
1601.12
1561.18
1601.13
1622.31
1622.12
1681.13
1683.18
1682.81
1704.58
1703.94
1661.85
1704.93
1726.74
1726.63
1687.09
1664.59
1705.74
1705.81
1726.66
1760.46
1785.04
1806.75
1806.34
1764.42
1767.60
1808.67
1808.34
1764.91
1785.88
34
SAVo
−845.5571
−845.5575
−1305.1723
−1305.1754
−1305.1750
−1305.1753
−1305.1763
−1305.1762
−1305.1625
−1305.1723
−1305.1767
−1305.1768
−1764.7857
−1764.7891
−1764.7901
−1764.7911
−1764.7910
−1764.7758
−1764.7930
−1764.7941
−1764.7939
−1764.7805
−1764.7787
−1764.7936
−1764.7906
−1764.7911
−2224.3979
−2224.4030
−2224.4041
−2224.4040
−2224.3887
−2224.3925
−2224.4072
−2224.4074
−2224.3909
−2224.4046
35
TEESolo
−0.5439
−0.5439
−0.7950
−0.5021
−0.3766
−0.3766
−0.4602
−0.7531
−1.0042
−0.7531
−0.4602
−0.5439
−0.7950
−0.4184
−0.4184
−0.4602
−0.7950
−1.1715
−0.3347
−0.2092
−0.4184
−0.7113
−0.5021
−0.2929
−0.7113
−0.5021
−0.6276
−0.5021
−0.3347
−0.5858
−0.9623
−0.4602
−0.1674
−0.2092
−0.5439
−0.3347
36
PolSSo
151.4608
153.5528
158.6154
160.7911
158.7410
158.6154
160.7911
160.7493
156.8582
160.8748
162.9250
162.8831
165.7701
165.8538
165.8119
167.9876
167.9458
163.8454
167.9039
170.1214
170.0796
166.2303
164.0128
167.9876
168.1131
170.2051
170.6235
172.9247
175.1004
175.0586
171.0419
171.1674
175.1841
175.1422
170.9582
173.0084
37
CEo
−10.1671
−10.2090
−11.1713
−11.1713
−11.1713
−11.1713
−11.2131
−11.1294
−11.1713
−11.2131
−11.2968
−11.5060
−12.1336
−12.0918
−12.1336
−12.2173
−12.1336
−12.1336
−12.0918
−12.1754
−12.1336
−12.1336
−12.1336
−12.0918
−12.0918
−12.2173
−13.0959
−13.0122
−13.1378
−13.0541
−13.0541
−13.0541
−13.0122
−13.0122
−13.0541
−13.1378
38
DEo
40
T(Cl-Cl)
141.2518
0
143.3438
0
147.4442
3
149.6198
4
147.5697
5
147.4442
5
149.5780
6
149.6198
5
145.6869
4
149.6617
3
151.6282
7
151.4190
6
153.6365
10
153.7202
12
153.6365
14
155.7703
16
155.8122
14
151.7118
12
155.8540
14
157.9042
16
157.9878
16
154.0967
14
151.8792
14
155.8958
16
156.0214
14
157.9878
16
157.5276
22
159.9125
26
162.0045
29
162.0045
28
157.9460
25
158.1134
27
162.1300
30
162.1300
29
157.9042
26
159.8706
30
(Continued on next page)
39
TNEo
590
30
CEw
184.14
179.58
184.43
182.17
179.33
186.56
182.21
186.82
184.81
177.61
184.43
187.02
186.44
191.17
191.42
193.76
189.20
194.05
191.79
189.03
189.28
191.88
187.02
194.10
191.63
188.87
195.81
198.53
193.59
201.04
201.00
198.91
198.53
198.70
196.48
196.36
205.43
202.92
209.33
29
PolSSw
−1.34
−2.43
−2.76
−2.93
−4.35
−0.63
−1.59
−1.63
−1.59
−2.34
−1.67
−2.59
−2.43
−1.42
−1.55
−0.84
−1.92
−1.97
−2.01
−3.47
−1.67
−0.75
−1.80
−0.92
−0.96
−1.97
−1.88
−0.84
−1.88
−0.84
−0.88
−0.92
−1.17
−2.34
−1.00
−1.05
−0.92
−0.96
−0.84
#CN
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
−33.51
−33.43
−33.56
−33.56
−33.43
−33.51
−33.39
−33.68
−33.68
−33.39
−33.39
−33.89
−35.52
−35.69
−35.69
−35.69
−35.52
−35.86
−35.86
−35.73
−35.73
−35.69
−35.61
−35.69
−35.69
−35.69
−37.87
−37.82
−37.74
−37.91
−37.82
−37.82
−37.82
−38.03
−37.87
−37.87
−39.96
−40.00
−42.17
31
DEw
150.67
146.19
150.92
148.66
145.98
153.09
148.87
153.22
151.21
144.31
151.08
153.18
150.96
155.56
155.77
158.11
153.72
158.24
156.02
153.34
153.59
156.23
151.46
158.45
156.02
153.22
157.99
160.75
155.90
163.22
163.26
161.13
160.75
160.75
158.66
158.53
165.56
162.97
167.23
32
TNEw
761.19
741.72
761.28
751.85
739.95
771.54
753.03
771.64
763.17
733.37
762.23
771.34
763.15
783.25
784.11
794.24
774.78
794.38
785.06
773.24
775.20
786.34
765.72
795.49
785.37
773.67
795.28
806.91
785.96
817.21
817.07
808.14
806.74
806.42
798.16
797.65
828.15
817.90
837.53
33
SASo
34
SAVo
1807.72
1765.14
1808.61
1787.77
1761.52
1830.14
1789.89
1831.01
1812.58
1747.36
1810.15
1830.90
1841.63
1885.86
1888.01
1909.90
1867.42
1910.94
1890.25
1864.24
1868.37
1892.74
1847.70
1912.98
1890.25
1864.52
1941.34
1966.98
1920.85
1990.13
1989.89
1970.47
1966.89
1966.58
1948.11
1946.82
2043.53
2020.08
2091.69
APPENDIX. Values of molecular descriptors nos. 31–40. (Continued)
−2224.4075
−2224.3916
−2224.4051
−2224.3934
−2224.3887
−2224.4102
−2224.3962
−2224.4079
−2224.3979
−2224.3779
−2224.4076
−2224.4053
−2683.9990
−2684.0159
−2684.0173
−2684.0201
−2684.0041
−2684.0179
−2684.0059
−2684.0012
−2684.0052
−2684.0096
−2683.9894
−2684.0211
−2684.0080
−2684.0033
−3143.6111
−3143.6158
−3143.5948
−3143.6296
−3143.6299
−3143.6175
−3143.6155
−3143.6135
−3143.6008
−3143.6005
−3603.2233
−3603.2055
−4062.8097
35
TEESolo
−0.2092
−0.5021
−0.6276
−0.6694
−1.1715
−0.0418
−0.2929
−0.2510
−0.2092
−0.4184
−0.3766
−0.4184
−0.5858
−0.2510
−0.2929
−0.0837
−0.4184
−0.3347
−0.3347
−0.8368
−0.2929
−0.0418
−0.3347
−0.1255
−0.1255
−0.4184
−0.3766
−0.0837
−0.3766
−0.0837
−0.0837
−0.1255
−0.2092
−0.4184
−0.0837
−0.1255
−0.0837
−0.0837
−0.0418
36
PolSSo
175.1004
171.0001
175.2678
173.2176
170.7490
177.2342
173.3431
177.4016
175.6025
169.2010
175.3096
177.4853
175.6862
179.9120
180.1212
182.2132
178.1129
182.3806
180.4141
177.9455
178.1966
180.4978
176.1882
182.4642
180.2886
177.8618
182.6316
185.0583
180.6233
187.2340
187.1922
185.3094
185.0165
185.1002
183.1755
183.0500
189.7026
187.5269
191.8782
37
CEo
−13.0541
−13.0959
−13.0541
−13.0959
−13.0959
−12.9704
−13.0122
−13.0541
−13.0959
−13.5562
−12.9704
−13.4725
−13.9746
−13.9327
−13.9327
−13.8490
−13.9327
−13.9746
−14.0164
−14.0164
−14.0164
−13.9327
−13.9746
−13.8490
−13.9327
−13.9746
−14.8950
−14.8114
−14.8950
−14.9369
−14.7695
−14.8114
−14.8114
−14.9369
−14.8532
−14.8532
−15.6900
−15.7737
−16.6523
38
DEo
162.0463
157.9042
162.2137
160.1217
157.6531
164.2638
160.3309
164.3475
162.5066
155.6448
162.3392
164.0128
161.7534
165.9793
166.1466
168.3223
164.2220
168.4060
166.3977
163.9291
164.1802
166.6069
162.1718
168.6152
166.3977
163.8873
167.7366
170.2470
165.7701
172.3390
172.4226
170.4980
170.2051
170.1633
168.2805
168.1968
174.0126
171.7532
175.2259
39
TNEo
29
28
30
29
26
30
29
31
30
28
30
32
42
46
48
48
46
50
48
46
48
48
46
50
48
46
69
70
67
73
73
72
71
72
71
70
100
98
132
40
T(Cl-Cl)