A value-added use of volatile turpentine: antifungal activity and QSAR study of β-pinene derivatives against three agricultural fungi

Article abstract of DOI:10.1039/c5ra10660e

In consideration of turpentine and its analogues possessing some agricultural biological activity, persistent efforts to take advantage of renewable, abundant natural resources have been made. Three series of derivatives from β-pinene were synthesized and their fungicidal activities against Rhizoctonia solani, Fusarium graminearum, and Botrytis cinerea were investigated. Most of the synthesized compounds exhibited moderate to significant fungicidal activity. Among them, the acylthiourea derivatives from β-pinene showed more promising results than the other compounds. It was worth noting that compounds 7b and 7d displayed excellent fungicidal activity against Rhizoctonia solani, with IC₅₀ values of 2.439 and 1.857 μg mL^-1, which was close to or even better than the control triadimenol (1.945 μg mL^-1, a commercial fungicide). The structure-activity relationship (SAR) analysis indicated that the compounds with more net positive charge possessed better fungicidal activity. The quantitative structure-activity relationship (QSAR) model (R² = 0.9879, F = 348.41, S² = 0.0047) was obtained through the best multi-linear regression. The built model revealed a strong correlation of fungicidal activity against Rhizoctonia solani with the molecular features of the title compounds. Additionally, the SAR and QSAR studies showed that the introduction of an electron-withdrawing group, which can increase the positive charge, was favorable towards the fungicidal activity. These encouraging results may provide an alternative, promising use of β-pinene through the design and exploration of eco-friendly fungicides with low toxicity and high efficiency.

Full text of DOI:10.1039/c5ra10660e

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A value-added use of volatile turpentine: antifungal activity
and QSAR study of β-pinene derivatives against three
agriculture fungi
Jian Li, &a Xiangrong Tian, &b Yanqing Gao,*b Shibin Shang,c Juntao Feng,b and
Xing Zhang b
Received (in XXX, XXX) Xth XXXXXXXXX 20XX, Accepted Xth XXXXXXXXX 20XX
DOI: 10.1039/b000000x
Abstract
In consideration of turpentine and its analogues possess some agricultural biological
activity, persistent efforts to take advantage of renewable, abundant natural resource
have been made. Three series of derivatives from βꢀpinene were synthesized and the
fungicidal activities against Rhizoctonia solani, Fusarium graminearum, and Botrytis
cinerea were investigated. Most of synthesized compounds exhibited moderate to
significant fungicidal activity. Among them, acylthiourea derivatives from βꢀpinene
showed more promising results than the other compounds. It was worth noting that
compounds 7b and 7d displayed excellent fungicidal activity against Rhizoctonia
solani, with values of IC50 2.439 and 1.857 µg/mL, which was close to or even better
than the control tiadimenol (1.945 µg/mL, a commercial fungicide). The
structureꢀactivity relationship (SAR) analysis indicated that the compounds with more
net positive charge possessed better fungicidal activity. The quantitative structure
ꢀactivity relationship (QSAR) model (R2 = 0.9879, F = 348.41, S2 = 0.0047) was
obtained through the best multiꢀlinear regression. The built model revealed a strong
1

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correlation of fungicidal activity against Rhizoctonia solani with molecular features of
the title compounds. Additionally, the SAR and QSAR studies showed that the
introduction of the electronꢀwithdrawing group, which can increase positive charge,
was in favor of the fungicidal activity. These encouraging results may enlighten with
an alternative, promising use of βꢀpinene as the design and exploration ecoꢀfriendly
fungicides with low toxicity and high efficiency.
Keywords: turpentine, βꢀpinene, fungicidal activity, QSAR
1. Introduction
In the development of modern crop production, ensuring both of food safety and
disease prevention is a key issue. In traditional agriculture, chemical fungicides were
used to protect crops from plant diseases. However, the abuse of single synthesized
fungicide has led to the development of resistance as well as increased side effects to
environment and harmless creatures.1ꢀ5 This is also one of the main reasons why
efficient and ecoꢀfriendly fungicides need to be explored.
As a remarkable alternative mean to classic agrochemicals, botanical fungicides
have played an increasingly important role in integrated and ecological disease
management.6 During the longꢀstanding interaction between plants and environment,
some secondary metabolites have been produced, which endow plants the ability to
withstand adversity. Namely, plant secondary metabolites can protect plants from pest
and diseases attacks. Currently, a range of secondary metabolites from natural sources
such as avonoids, alkaloids and terpenoids have been developed as the lead
compounds for the preparation of potent fungicides of less or slower resistance and
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lower pollution.7, 8
Turpentine is a renewable and abundant natural resource commodity in China. As
a secondary metabolite secreted from some species of pine, turpentine have been
proposed to have potential activity.9 As shown in Scheme 1, βꢀpinene is an important
component of turpentine. Based on this promising reality, βꢀpinene can be used as
lead active material in some fields, such as natural resource in pharmaceutical industry,
insecticides and fungicide in crop protection, repellents in health care,
polyterpineꢀresin in polymer materials, gum arabic food, and solvent in paint and
11
varnish.10,
βꢀpinene analogues have been reported to have broadꢀspectrum
bioactivity, such as antibacterial,12ꢀ15 antifeedant,16ꢀ23 repellent,24ꢀ27 and antifungal.28
Among these analogues, cumic acid is a transformant of βꢀpinene in plants and it has
good antimicrobial activity.29 Encouragingly, the dehydrocumic acid, which almost
has the same molecular structure as cumic acid, can be chemically synthesized from
βꢀpinene.30 It was expected to develop some ecoꢀfriendly fungicide of high efficiency
and selectivity from dehydrocumic acid.
In order to obtain novel natural productꢀbased fungicides, three series of
derivatives from βꢀpinene were synthesized on the basis of molecular similarity. The
fungicidal activities of the title compounds against three important agricultural
pathogens Rhizoctonia solani (R. solani), Fusarium graminearum (F. graminearum)
and Botrytis cinerea (B. cinerea) were investigated. Moreover, the QSAR was also
performed on all of the title compounds using the Gaussian and CODESSA sofware
packages, which can account for the structural features responsible for the fungicidal
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activity. This exploration was expected to improve the added value of βꢀpinene as
botanical fungicides in organic agriculture.
2. Experimental sections
2.1. Synthetic procedures and identification
βꢀpinene (1) was obtained from a commercial source (Wu Zhou Pine Chemicals Ltd.,
Guangxi, China) and used without further purification. All other chemicals used were
of reagent grade. The Fourier transform infrared (FTꢀIR) spectra of dried compounds
were measured on a Nicolet IS10 (Nicolet, Madison, USA) spectrophotometer, using
1
a KBr pellets technique. The H NMR spectra were recorded on a Bruker AVꢀ300
(Bruker, Karlsruhe, Germany) nuclear magnetic resonance spectrometer with CDCl3
or DMSOꢀd6 as the solvent and TMS as an internal standard. The MS spectra were
taken on an Agilentꢀ5973 (Agilent, Santa Clara, USA) spectrophotometer. ESI mass
spectral data were obtained on a Bruker QꢀTOF mass spectrometer, equipped with
electrospray ionization source, operated in positive ion mode. The elemental analysis
(C, H, and N) was carried out on a Vario ELꢀIII (Elementar, Hanau, Germany)
elemental analyzer and the results were in good agreement with the calculated values.
Xꢀray intensity data were collected on a Bruker/EnrafꢀNonius CADꢀ4 (Bruker,
Karlsruhe, Germany) diffractometer. Melting points were taken on XTꢀ5 (Saiao,
Beijing, China) and uncorrected. All reactions were traced by thinꢀlayer
chromatography (TLC) analysis, which was performed using Merck silica gel 60
GF254 plates, visualized under 254 nm UV light and eluted with a gradient of
petroleum ether/acetone (v/v = 8:1). The preparation of title compounds was shown in
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Scheme 2, and the substituted groups of βꢀpinene derivatives were shown in Table 1.
The pathogenic fungi were provided by research & development center of biorational
pesticide, Northwest A & F University.
Table 1 The substituted groups of βꢀpinene derivatives
Compd. X1
X2 X3
R1
R2
Compd. X1
X2 X3
R1
R2
5a
5b
5c
5d
5e
5f
N
ꢀ
ꢀ
ꢀ
ꢀ
ꢀ
ꢀ
ꢀ
ꢀ
ꢀ
ꢀ
ꢀ
ꢀ
ꢀ
C6H5
oꢀCH3C6H4
mꢀCH3C6H4
pꢀCH3C6H4
2,4ꢀ methylphenyl
3,5ꢀ methylphenyl
C6H5
ꢀ
ꢀ
ꢀ
ꢀ
ꢀ
ꢀ
ꢀ
ꢀ
ꢀ
ꢀ
ꢀ
7f
8a
8b
8c
8d
8e
8f
C=S
N
N
N
N
N
N
N
N
N
N
N
ꢀ
2,4ꢀ methylphenyl
ꢀ
N
N
O
O
O
O
O
O
O
O
O
O
C
C
C
C
C
C
C
C
C
C
CH3
CH3
funanꢀ2ꢀyl
thiopheneꢀ2ꢀyl
4ꢀClC6H4
4ꢀCH3C6H4
cyclohexyl
pyridine
CH3
ꢀ
N
ꢀ
CH3
N
ꢀ
CH3
N
ꢀ
cyclohexyl
H
7a
7b
7c
7d
7e
C=S
C=S
C=S
C=S
C=S
N
N
N
N
N
benzyl
8g
8h
8i
CH3
oꢀCH3C6H4
mꢀCH3C6H4
pꢀCH3C6H4
CH3
CH2CH3
C6H5
H
8j
CH3
C6H5
2.1.1. Synthesis of 2-hydroxy-6, 6-dimethylbicyclo [3.1.1] heptane-2-carboxylic
acid (2). βꢀpinene was oxidized by basic potassium permanganate in the
homogeneous solution water/tertꢀbutanol (v/v = 1:2). KMnO4 (12.0 g, 0.076 mol) and
NaOH (1.5 g, 0.038 mol) were dissolved in the mixture of water (100 mL) and
tꢀBuOH (50 mL). While stirring vigorously, βꢀpinene (5.2 g, 0.038 mol) was added.
The reaction was maintained at 15ꢀ25 °C for 0.5 h, and then was heated to 80 °C.
After hot filtration, cooling, acidification, and vacuum drying, the crude compound
31
was precipitated.
The target chemical 2 was obtained by recrystallization with
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ethanol/toluene to give colorless crystals (0.30 mm × 0.20 mm × 0.10 mm in size)
suitable for Xꢀray single crystal diffraction. The crystal structure was solved by direct
methods and refined on F2 using all data by fullꢀmatrix least squares procedures with
SHELXS 97.
2.1.2. Synthesis of 4-isopropylcyclohexa-1, 3-dienecarboxylic acid (3). A
solution of 40 g concentrated sulfuric acid in 100 mL water was added dropwise into a
solution of compound 2 (10.0 g, 0.054 mol) at room temperature. Then the reaction
was heated to 75 °C. After reacting for 6 h, the mixture was recrystallized with
water/ethanol (v/v = 1:1) to give a white flaky solid 3.
2.1.3. Synthesis of 4-isopropylcyclohexa-1, 3-dienecarbonyl chloride (4). In a
250 mL flask with waterꢀcooled condenser, thermometer, drying tube and dropping
funnel, compound 3 (5.0 g, 0.027 mol) was dissolved in CH2Cl2 (50 mL). Thionyl
chloride (0.082 mmol) was added dropwise within 1h and refluxed for 4 h at 65 °C.
The chloride (4) was obtained after removing the solvent and excess thionyl chloride.
2.1.4. Synthesis of 4-isopropylcyclohexa-1, 3-dienecarboxamides (5a-f). A
solution of the above chloride (4) in 10 mL of CH2Cl2 was added dropwise to a
solution of 40 mmol of amine and 40 mmol of triethylamine in 40 mL of CH2Cl2
within 30 minutes at room temperature. After reacting for 12 h, the residue was dried
with anhydrous MgSO4 and purified by ethanol crystallization to give the six resulting
compounds 5aꢀf.13
2.1.5. Synthesis of 4-isopropylcyclohexa-1, 3-dienecarboxthioureas (7a-f). A
solution of KSCN (3.0 g, 0.03 mol) and anhydrous 100 mL CH2CN was added to a
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250 mL flask equipped with waterꢀcooled condenser, thermometer, drying tube, and
dropping funnel. After reacting for 24 h at 70 °C, the intermediate compound 6 was
collected by filtration and used without further purification. Then the arylamine (0.03
mol) was added and the resulting mixture was refluxed for 2 h. The six resulting
compounds 7aꢀf were obtained after drying with anhydrous MgSO4 and purification
by silica gel chromatography [ethyl acetate / petroleum ether (v/v = 1:10)].32
2.1.6. Synthesis of oximyl 4-isopropylcyclohexa-1, 3-dienecarboxylates (8a-j).
A solution of dihydrocumyl chloride in 50 mL of CH2Cl2 was added dropwise to a
solution of 0.030 mol of oxime and 0.040 mol of triethylamine in 40 mL of CH2Cl2
within 30 minutes at 15ꢀ25 °C. After that, the reaction mixture was kept at room
temperature for 2 h and washed with distilled water. The residue was dried with
anhydrous MgSO4 and purified by silica gel chromatography [ethyl acetate/petroleum
ether (v/v = 1:10)] to give the ten resulting compounds 8aꢀj.30
2.2. Biological assay
The fungicidal effect was determined by inhibition of mycelia radial growth. The test
chemicals were dissolved in DMSO at various concentrations (2560 ꢀg/mL, 1280
ꢀg/mL, 640 ꢀg/mL, 320 ꢀg/mL, 160 ꢀg/mL). The solutions were diluted into one tenth
with PotatoꢀDextrose Agar (PDA) at 50 °C to give the required drugꢀcontaining
medium at series of concentrations (256 ꢀg/mL, 128 ꢀg/mL, 64 ꢀg/mL, 32 ꢀg/mL, 16
ꢀg/mL). Evaluations were based on a percentage scale of 0ꢀ100, where 0 = no activity
and 100 = complete eradication. The standard deviations of the tested biological
values were ± 5%. The IC50 values were calculated by probit analysis. For
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comparative purposes, the commercial product thiadimenol was tested under the same
conditions.
Petri dishes containing 10 mL of the drugꢀcontaining agar medium were
inoculated by placing 6 mm fungusꢀcoated discs upside down on the agar surface. The
petri dishes containing 10 mL DMSO were set as the negative control. Plates were
incubated at 30 °C and the mycelia radial growth was measured after three days. The
inhibition rate was calculated by/(%) = (TzoneꢀCzone)/Tzone × 100, where Tzone was the
inhibition zone of test compounds, and Czone was the inhibition zone of the negative
control. The IC50 values of compounds were calculated with SPSS Statistic program
version 17.0.33
2.3 Building and validation of the QSAR model
The general procedure of QSAR model building and validation was shown in Fig. 1.
Firstly, the optimal conformers with the lowest energy of the title compounds were
performed at the DFT/6ꢀ31G (d) level using a Gaussian 03W package of programs.34
Secondly, the calculated results were changed into a form compatible with CODESSA
2.7.15 using Ampac 9.1.3. Thirdly, all the molecular descriptors involved in these
compounds were calculated by CODESSA 2.7.15. In order to find out which
structural features play an important role in fungicidal activity against R. solani, the
best multiple regression analysis was selected to generate the QSAR model. In this
model, the statistical criteria were indicated by the squared correction coefficient (R2),
the squared standard error of the estimates (S2), and the Fisher significance ratio (F).
The tested IC50 values were converted into the corresponding log IC50 and used as
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dependent variables in the QSAR studies. The quality of the final model was
determined using both an internal validation and the “leaveꢀoneꢀout” crossꢀvalidation
methods.
3. Results and discussion
3.1. Synthesis
Polymerization reaction easily took place due to inappropriate temperature, time, and
concentration of H2SO4 in the synthesis of compound 3. During the reaction process,
too high temperature led to the appearance of yellow and globular material; too long
reaction time led to the dark product. The white flake solid (compound 3) was
obtained with the optimum reaction condition: 80 °C, 20% H2SO4, and 6 h. Infrared
spectrum data showed that the absorption peak of hydroxyl (3100 cmꢀ1) disappeared,
and carbonꢀcarbon double bond (1572 cmꢀ1) appeared.
The oilꢀsoluble acyl chloride and waterꢀsoluble KSCN could not react
homogeneously. The anhydrous CH2CN was chosen to make a homogeneous reaction.
The intermediate isothiocyanate needed not to be purified and used directly in the
synthesis of acylthiourea derivatives from βꢀpinene (7aꢀf).
The preparation of oximes played an important role in the synthesis of title
compounds 8aꢀj. According to the literatures reported, the yield and compatibility of
the reaction were affected by the solvent and base.35 At present work, the ethanol and
sodium carbonate were selected as the suitable solvent and base in preparing
compounds (8aꢀj).
3.2. Crystal structure analysis
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The target chemical 2 was obtained by recrystallization with ethanol/toluene to give
colorless crystals suitable for Xꢀray single crystal diffraction, and the XRD peaks
observed in the value of 2θ range of 10ꢀ13 °C, θmax = 25.3 °C, θmin = 1.6 °C. The
crystallographic parameters was shown as: a = 26.796 (5) Å, b = 6.6560 (13) Å, c =
12.250 (3) Å, β = 112.23 (3)º, V = 2022.5 (9) Å3, Z = 8, ꢀ = 0.009 mmꢀ1, T = 293 K, R
[F2 > 2σ (F2)] = 0.056, wR (F2) = 0.153, S = 1.00, λ = 0.71073 Å, F000 = 800.
3.3. Fungicidal activity and structureꢀactivity relationships (SAR)
3.3.1. Fungicidal activity of title compounds against R. solani. The results of
fungicidal activity against R. solani were summarized in Table 2, from which it can
be seen that the title compounds exhibited certain fungicidal activity against R. solani.
The introduction of acyl thiourea group increased fungicidal activity against R. solani
significantly. Compounds 7aꢀf exhibited about 90% inhibition rate against R. solani at
64 µg/mL. It was notable that the IC50 values of 7b and 7d were similar to or lower
than thiadimenol, a commercialized fungicide. Compared with compound 3, all the
derivatives had more significant fungicidal activity. There were several postulations
about mechanisms elucidating the antimicrobial activity.36 The most acceptable
mechanism is the interaction between positively charged molecules and negatively
charged microbial cell membrane. The compounds having protonated groups can
generate electrostatic forces with the electrostatic negative charges on the microbial
cell surfaces.37 Based on this electrostatic interaction mechanism, the analogues with
more positive charge would possess higher antimicrobial activity. The introduction of
amide, acyl thiourea, and oxime groups into compound 3 increased its cationic centers
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and its solubility in organic and aqueous media, which strengthened the net positive
charge and leaded to a better fungicidal activity. In regard to different kinds of
derivatives, the fungicidal activity level was acyl thiourea (7aꢀf) > oxime ester (8aꢀj) >
amide (5aꢀf), which complied with above mechanism; for the same kind of derivatives,
the activity results also met the mechanism well. Take oxime derivatives from
βꢀpinene as example, compounds 8c, 8i, 8j, and 8d, with electronꢀwithdrawing group
has better activity than other compounds with electronꢀdonating groups.
Table 2 Fungicidal activity of compounds against R. solani
Fungicidal activity (%) at a concentration of (µg/mL)
NO.
Compd.
256
128
64
32
16
IC50
y=ax+b
R2
log IC50
1
3
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
67
70
70
72
70
70
72
98
98
97
98
97
97
95
95
95
95
93
95
95
94
95
95
98
40
53
50
53
50
48
54
89
89
87
89
87
86
75
76
80
76
70
75
74
72
79
77
89
20
31
30
30
30
30
33
76
77
73
78
73
72
53
55
61
57
50
54
52
51
60
58
76
0
92.712
69.777
72.485
68.481
72.485
73.144
66.345
3.017
y = 0.236 x ꢀ 2.722
y = 0.175 x ꢀ 1.445
y = 0.177 x ꢀ 1.528
y = 0.177 x ꢀ 1.456
y = 0.177 x ꢀ 1.528
y = 0.177 x ꢀ 1.525
y = 0.174 x ꢀ 1.382
y = 0.235x ꢀ 0.102
y = 0.236 x ꢀ 0.083
y = 0.221 x ꢀ 0.099
y = 0.237 x ꢀ 0.063
y = 0.221 x ꢀ 0.099
y = 0.218 x ꢀ 0.112
y = 0.206 x ꢀ 1.089
y = 0.207 x ꢀ 1.051
y = 0.211 x ꢀ 0.948
y = 0.206 x ꢀ 1.011
y = 0.200 x ꢀ 1.155
y = 0.206 x ꢀ 1.069
y = 0.207 x ꢀ 1.138
y = 0.201 x ꢀ 1.114
y = 0.211 x ꢀ 0.995
y = 0.208 x ꢀ 1.026
y = 0.235 x ꢀ 0.065
0.977
0.952
0.971
0.964
0.971
0.986
0.961
0.990
0.987
0.990
0.983
0.990
0.994
0.991
0.985
0.957
0.980
0.984
0.989
0.992
0.992
0.960
0.972
0.992
1.967
1.844
1.860
1.836
1.860
1.864
1.822
0.480
0.387
0.516
0.269
0.516
0.576
1.513
1.497
1.439
1.484
1.567
1.507
1.530
1.546
1.459
1.480
0.289
2
5a
24
23
25
23
24
25
58
58
57
58
57
57
33
33
33
33
30
33
32
32
32
32
59
3
5b
4
5c
5
5d
6
5e
7
5f
8
7a
9
7b
2.439
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
7c
3.284
7d
1.857
7e
3.284
7f
3.770
8a
32.604
31.371
27.470
30.444
36.840
32.146
33.902
35.174
28.749
30.230
1.945
8b
8c
8d
8e
8f
8g
8h
8i
8j
Thiadimenol
3.3.2. Fungicidal activity of title compounds against F. graminearum. The results
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of fungicidal activity of the title compounds against F. graminearum were listed in
Table 3, from which it was shown that compounds 3, 5aꢀf, 7aꢀf and 8aꢀj exhibited low
fungicidal activity against F. graminearum. The death rate of most compounds was
about 30ꢀ40% at 256 µg/mL, which was less effective than fungicidal activity against
R. solani.
Table 3 Fungicidal activity of compounds against F. graminearum
Fungicidal activity (%) at
Fungicidal activity (%) at
Compd.
a concentration of (µg/mL)
Compd.
a concentration of (µg/mL)
256
128
0
64
256
43
128
64
3
20
30
30
30
30
30
30
44
45
43
45
30
ꢀ
7f
20
20
20
20
20
20
20
20
20
20
20
100
0
0
5a
5b
5c
5d
5e
5f
10
10
10
10
10
10
20
20
20
20
20
0
0
0
0
0
0
0
0
0
0
0
8a
38
40
40
40
40
40
37
36
40
40
100
8b
0
8c
0
8d
0
8e
0
8f
0
7a
7b
7c
7d
7e
8g
0
8h
0
8i
8j
0
0
Thiadimenol
89
3.3.3. Fungicidal activity of title compounds against B. cinerea. The results of
fungicidal activity of the title compounds against B. cinerea were listed in Table 4,
from which it can be seen that all of the test compounds exhibited low fungicidal
activity against B. cinerea. These compounds have lowest fungicidal activity against
B. cinerea, and the death rate was about 20ꢀ30% at 256 µg/mL.
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Table 4 Fungicidal activity of compounds against B. cinerea
Fungicidal activity (%) at
Fungicidal activity (%) at
a concentration of (µg/mL)
Compd.
a concentration of (µg/mL)
Compd.
256
10
20
20
20
20
20
20
34
35
33
35
30
128
0
64
ꢀ
256
33
38
30
30
30
30
30
27
26
30
30
100
128
20
10
10
10
10
10
10
10
10
10
10
100
64
0
3
7f
5a
5b
5c
5d
5e
5f
10
10
10
10
10
10
20
20
20
20
20
0
0
0
0
0
0
0
0
0
0
0
8a
0
8b
0
8c
0
8d
0
8e
0
8f
0
7a
7b
7c
7d
7e
8g
0
8h
0
8i
8j
0
0
Thiadimenol
89
3.4 QSAR study on fungicidal activity against R. solani
In QSAR study, the selection of structure descriptors was an important step to
determine molecular features correlating the activity. The descriptors can be
categorized as constitutional, topological, geometrical, electrostatic, quantum
chemical, and thermodynamic. In this research, the most important factors were
geometry and charge distribution, which involved some descriptors, such as HOMO
energy (the energy of the highest occupied molecular orbital), LUMO energy (the
energy of the lowest occupied molecular orbital), dipole moment, etc. Many
regression approaches, such as best multiꢀlinear, multiꢀlinear regression, principal
component analysis, partial least square regression, and heuristic regression can be
available in CODESSA 2.7.15 software. In view of number of samples and descriptors
involved in this work, the best multiꢀlinear regression was chosen.
To determine the number of descriptors is an another important step. A simple
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rule called “breaking point” was used in the improvement of the statistical quality of
the model. As shown in Fig. 2, the R2 value of the best multiꢀlinear regression had a
dramatic increase before the number of the descriptors was up to 4. The significance
of the descriptors within the model was reflected in the tꢀtest. Descriptors with high t
values were accepted and those with low t values were rejected. After the number of
the descriptors reached a certain value, the improvement of the regression model
became less insignificant (ꢁR2
＜ 0.02ꢀ0.04). Furthermore, another point worth
noting was that the number of the descriptors conformed to the linear regression given
by Eq. (1)
N ≥ 3 (k + 1)
(1)
Where N is the number of samples and k is the number of descriptors. Consequently,
the final QSAR model with four descriptors was selected as the best model. The
values of four descriptors of sample compounds were listed in Table 5.
The best statistical model for the log IC50 data had the following statistical
characteristic: R2 = 0.9879, F = 348.41, S2 = 0.0047. This model included four
descriptors in descending order according to their statistical significance, which were
shown in Table 6, and the regression coefficients X and their standard errors ꢁX were
also listed.
The values of experimental and predicted log IC50 were listed in Table 7, and the
plot of the comparision between the predicted and experimental values was shown in
Fig. 3. The fourꢀdescriptor QSAR model equation was described in the following Eq.
(2)
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log IC50 =
－
8.3866
－
39.598 × HOMO
30.670 × qmin
N = 22, R2 = 0.9879, F = 348.41, S2 = 0.0047
－
19.346 × DM + 8.8104 × qO
+
max
(2)
Table 5 Fungicidal activity and structural descriptors of the title compounds
Structure descriptors
No.
Compd.
log IC50
HOMO
ꢀ0.3512
ꢀ0.3534
ꢀ0.3523
ꢀ0.3525
ꢀ0.3546
ꢀ0.3534
ꢀ0.2713
ꢀ0.2710
ꢀ0.2725
ꢀ0.2718
ꢀ0.2718
ꢀ0.2731
ꢀ0.2896
ꢀ0.2917
ꢀ0.2922
ꢀ0.2950
ꢀ0.2963
ꢀ0.2873
ꢀ0.2945
ꢀ0.2970
ꢀ0.2907
ꢀ0.2940
DM
qO
qmin
max
1
5a
5b
5c
5d
5e
5f
1.844
1.860
1.836
1.860
1.864
1.822
0.480
0.387
0.516
0.269
0.516
0.576
1.513
1.497
1.439
1.484
1.567
1.507
1.530
1.546
1.459
1.480
ꢀ0.2067
ꢀ0.2067
ꢀ0.2067
ꢀ0.2067
ꢀ0.2066
ꢀ0.2067
ꢀ0.2072
ꢀ0.2070
ꢀ0.2072
ꢀ0.2072
ꢀ0.2072
ꢀ0.2072
ꢀ0.2095
ꢀ0.4339
ꢀ0.2071
ꢀ0.2070
ꢀ0.2070
ꢀ0.2073
ꢀ0.2086
ꢀ0.2071
ꢀ0.2072
ꢀ0.2071
0.3524
0.3523
0.3524
0.3520
0.3519
0.3521
0.2748
0.2522
0.2745
0.2748
0.2749
0.2746
0.3705
0.3685
0.3706
0.3696
0.3697
0.3706
0.3698
0.3691
0.3697
0.3697
ꢀ0.3512
ꢀ0.3534
ꢀ0.3523
ꢀ0.3525
ꢀ0.3546
ꢀ0.3534
ꢀ0.2713
ꢀ0.2768
ꢀ0.2725
ꢀ0.2718
ꢀ0.2718
ꢀ0.2731
ꢀ0.2896
ꢀ0.4339
ꢀ0.2922
ꢀ0.2950
ꢀ0.2963
ꢀ0.2873
ꢀ0.2945
ꢀ0.2970
ꢀ0.2907
ꢀ0.2940
2
3
4
5
6
7
7a
7b
7c
7d
7e
7f
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
8a
8b
8c
8d
8e
8f
8g
8h
8i
8j
Table 6 The best fourꢀdescriptor model
Descriptor No.
X
±ꢁX
tꢀTest
ꢀ5.5119
ꢀ3.3086
ꢀ2.5843
19.4733
2.5738
Descriptor
0
1
2
3
4
ꢀ8.3866
1.5216
Intercept
HOMOa
ꢀ3.9598×101
ꢀ1.9346×101
8.8104
1.1968×101
7.4859
DMb
4.5244×10ꢀ1
1.1916×101
qO
c
max
3.0670×101
qmin
d
a Energy of the highest occupied molecular orbit in atomatic units. b Dipole moment. c Max net atomic charge for a O atom. d Min net
atomic charge.
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Table 7 The difference between the experimental log IC50 and predicted log IC50
NO.
Compd.
Calc. log
IC50
Exp. log
IC50
Difference
NO.
Compd.
Calc. log
IC50
Exp. log
IC50
Difference
1
2
5a
5b
5c
5d
5e
5f
1.8522
1.9670
ꢀ0.1148
0.0270
0.0020
0.0243
0.0122
0.0472
ꢀ0.0151
0.0009
ꢀ0.0430
0.2004
ꢀ0.0458
12
13
14
15
16
17
18
19
20
21
22
7f
8a
8b
8c
8d
8e
8f
0.4792
0.4792
0.5760
1.5130
1.4970
1.4390
1.4840
1.5670
1.5070
1.5300
1.5460
1.4590
1.4800
1.8710
1.8620
1.8603
1.8762
1.8692
0.4649
0.3879
0.4730
0.4694
0.4702
1.8480
1.8600
1.8360
1.8640
1.8220
0.4800
0.3870
0.5160
0.2690
0.5160
1.5159
1.4970
1.4936
1.5078
1.5203
1.4537
1.5361
1.5232
1.4742
1.5017
1.5159
1.4970
1.4936
1.5078
1.5203
1.4537
1.5361
1.5232
1.4742
1.5017
3
4
5
6
7
7a
7b
7c
7d
7e
8
8g
8h
8i
9
10
11
8j
The developed QSAR model was validated by both internal validation and
“leaveꢀoneꢀout” crossꢀvalidation methods. In the internal validation, the compounds
were divided into three subsets A, B, and C. The compounds 1, 4, 7, 10, etc., belonged
to subset A; 2, 5, 8, 11, etc., belonged to subset B; and 3, 6, 9, 12, etc., belonged to
subset C. Two subsets, (A and B), (B and C), or (A and C) were selected as the
training set, and the remaining subset was treated as the test set. The correlation
equation, obtained from each of the training sets using the same descriptors, was used
to predicted values of the corresponding test sets. Internal validation results were
listed in Table 8.
Table 8 Internal validation of the QSAR model
Training set
A+B
N
R2
F
S2
Test set
N
7
8
7
R2
F
S2
15
14
15
0.9800
0.9711
0.9724
0.9745
340.78
339.58
339.63
340.00
0.0043
0.0041
0.0050
0.0045
C
A
0.9843
0.9814
0.9793
0.9817
345.90
343.25
341.47
343.54
0.0045
0.0044
0.0043
0.0044
B+C
A+C
B
Average
Average
a Compounds A: 1, 4, 7, 10, 13, 16, 19, 22, Compounds B: 2, 5, 8, 11, 14, 17, 20, Compounds C: 3, 6, 9, 12, 15, 18, 21.
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2
2
The difference between RTraining and RTest were within 5% for the three sets, and
2
2
the average values of RTraining = 0.9745 and RTest = 0.9817 were very similar to the
integrated R2 value, which signified the obtained model possessed the predictive
power of threeꢀfold crossꢀvalidation. In a similar way to the internal validation, the
“leaveꢀoneꢀout” method can be implemented. Every fourth compounds 1, 5, 9, 13, 17,
21 were put into an external test set, and the remaining compounds were left in the
training set. With the same four descriptors, the R2 of training set was 0.9640, and the
R2 of the test set was 0.9533, which also indicated that the obtained QSAR model was
of satisfactory.
Some structural features could make a difference to fungicidal activity by
interpreting the descriptors involved in the QSAR model. The first important
descriptor was the HOMO energy, which is directly related to the ionization potential
of the compounds.38, 39 In Fig. 4, the HOMO energy maps for compounds 7b and 7d
were shown. In the Eq. (2), the HOMO energy and activity was negatively correlated,
which suggested that the electron withdrawing substitution groups of derivatives are
beneficial for the fungicidal activity against R. solani. The consequence obtained from
QSAR study partially met the above SAR study result.
The second important descriptor was dipole moment, which is a measure of the
polarization between the positive and negative electrical charges in a system.40, 41 The
C=O, C=S, and NꢀH group exhibited permanent polarization because of a significant
electronegative difference between atoms. Among the title compounds, acylthiourea
derivatives from βꢀpinene displayed the better fungicidal activity, which illustrated the
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dipole moment played a critical role in modulating the activity.
The 3rd and 4th important descriptors were max net atomic charge for an O atom
and min net atomic charge. Both of these two descriptors can give an expression on
the features of the charge distribution in the molecules. In Eq. (2), appearance with a
positive sign in the model indicated that molecule with higher descriptor value had a
higher log IC50. In contrary, a negative sign in the model indicated that molecule with
lower descriptor value had a higher log IC50.
In summary, both the SAR and QSAR studies indicated that the electron
withdrawing substitution groups of βꢀpinene derivatives had positive effect for the
fungicidal activity. In the light of these results, the further research on the correlation
of SAR and QSAR is expected to carry on, and the design and exploration of potential
efficient fungicides would be implemented.
4. Conclusions
In purpose of increasing added value of turpentine，three series of βꢀpinene analogues
were prepared and their fungicidal activity against three common agricultural fungi
were determined. The acylthiourea derivatives from βꢀpinene 7aꢀf displayed more
significant activity than the other derivatives. It was worth noting that compounds 7b
and 7d exhibited excellent activity. Both the SAR and QSAR studies indicated the
structural features had apparent influence on the fungicidal activity. From these
satisfying consequences, some insight into the exploitation of ecoꢀfriendly fungicides
from natural products with low toxicity and high efficiency can be gained.
Acknowledgements
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The present research was supported by the National Nature Science Foundation
of China (Project No. 31401783 and Project No. 31400509) and Research Fund for
the Doctoral Program of Higher Education of Northwest A&F University (Project No.
Z109021501).
Notes and references
a
College of Forestry, Northwest Agriculture and Forestry University, Yangling,
Shaanxi 712100, People’s Republic of China.
b
Research & Development Center of Biorational Pesticide, College of Plant
Protection, Northwest Agriculture and Forestry University, Yangling, Shaanxi
712100, People’s Republic of China.
c Institute of Chemical Industry of Forest Products, Chinese Academy of Forestry,
Nanjing, Jiangsu 210042, People’s Republic of China.
&The authors contributed equally to this work and should be considered coꢀfirst
authors.
*Corresponding author: Telephone: +86ꢀ29ꢀ87091977. Fax: +86ꢀ29ꢀ87082392. Eꢀmail:
gaoyanqinggc@nwsuaf.edu.cn.
1
† Electronic Supplementary Information (ESI) available: IR, H NMR, MS, and
elemental analysis data for the target compounds. See DOI:10.1039/b000000x/
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Figure captions
Scheme 1 Chemical structures of major constitutes of turpentine
Scheme 2 Preparation of title compounds
Fig. 1 The general procedure of QSAR model building and validation
Fig. 2 The “breaking point” rule results
Fig. 3 Experimental log IC50 versus predicted log IC50
Fig. 4 Optimized structures and HOMO energy maps for compounds 7b and 7d
from DFT calculation of Gaussian 03W. The green parts represent positive
molecular orbitals, and the red parts represent negative molecular orbitals
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The general procedure of QSAR model building and validation
199x50mm (96 x 96 DPI)

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The breaking point” rule results
618x437mm (96 x 96 DPI)

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Experimental log IC50 versus predicted log IC50
297x210mm (200 x 200 DPI)

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Optimized structures and HOMO energy maps for compounds 7b and 7d from DFT calculation of Gaussian
03W. The green parts represent positive molecular orbitals, and the red parts represent negative molecular
orbitals
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Chemical structures of major constitutes of turpentine
91x72mm (96 x 96 DPI)