Synthesis and in vitro antioxidant activity evaluation of 3-carboxycoumarin derivatives and qsar study of their dpph radical scavenging activity

Article abstract of DOI:10.3390/molecules171214882

The in vitro antioxidant activities of eight 3-carboxycoumarin derivatives were assayed by the quantitative 1,1-diphenyl-2-picrylhydrazil (DPPH) radical scavenging activity method. 3-Acetyl-6-hydroxy-2H-1-benzopyran-2-one (C1) and ethyl 6-hydroxy-2-oxo-2H-1-benzopyran-3-carboxylate (C2) presented the best radical-scavenging activity. A quantitative structure-activity relationship (QSAR) study was performed and correlated with the experimental DPPH scavenging data. We used structural, geometrical, topological and quantum-chemical descriptors selected with Genetic Algorithms in order to determine which of these parameters are responsible of the observed DPPH radical scavenging activity. We constructed a back propagation neural network with the hydrophilic factor (Hy) descriptor to generate an adequate architecture of neurons for the system description. The mathematical model showed a multiple determination coefficient of 0.9196 and a root mean squared error of 0.0851. Our results shows that the presence of hydroxyl groups on the ring structure of 3-carboxy-coumarins are correlated with the observed DPPH radical scavenging activity effects.

Full text of DOI:10.3390/molecules171214882

Molecules 2012, 17, 14882-14898; doi:10.3390/molecules171214882
OPEN ACCESS
molecules
ISSN 1420-3049
www.mdpi.com/journal/molecules
Article
Synthesis and in Vitro Antioxidant Activity Evaluation of
3-Carboxycoumarin Derivatives and QSAR Study of Their
DPPH• Radical Scavenging Activity
Francisco J. Martínez-Martínez 1,*, Rodrigo Said Razo-Hernández 1, Ana Lilia Peraza-Campos 1,
Manuel Villanueva-García 2, Maria Teresa Sumaya-Martínez 3, Daniel Jaramillo Cano 1 and
Zeferino Gómez-Sandoval 1,*
1
Facultad de Ciencias Químicas, Universidad de Colima, kilómetro 9 carretera Colima-Coquimatlán,
Col., México, C.P. 28400, Mexico; E-Mails: rrazo@ucol.mx (R.S.R.-H.); peraza@ucol.mx (A.L.P.-C.);
jaracano@ucol.mx (D.J.C.)
2
Asociación de Jubilados de la Universidad de Guanajuato, Paseo de la Presa No 77, Guanajuato,
Gto., México, C.P. 36000, Mexico; E-Mail: villagm@ugto.mx
3
Secretaria de Investigación y Posgrado, Universidad Autónoma de Nayarit,
Ciudad de la Cultura “Amado Nervo”, Boulevard Tepic-Xalisco S/N, Tepic, Nayarit CP 63190,
Mexico; E-Mail teresumaya@hotmail.com
* Authors to whom correspondence should be addressed; E-Mails: fjmartin@ucol.mx (F.J.M.-M.);
zgomez@ucol.mx (Z.G.-S.); Tel./Fax: +52-312-3161163 (F.J.M.-M.).
Received: 21 September 2012; in revised form: 3 December 2012 / Accepted: 5 December 2012 /
Published: 13 December 2012
Abstract: The in vitro antioxidant activities of eight 3-carboxycoumarin derivatives were
assayed by the quantitative 1,1-diphenyl-2-picrylhydrazil (DPPH•) radical scavenging
activity method. 3-Acetyl-6-hydroxy-2H-1-benzopyran-2-one (C1) and ethyl 6-hydroxy-2-
oxo-2H-1-benzopyran-3-carboxylate (C2) presented the best radical-scavenging activity. A
quantitative structure-activity relationship (QSAR) study was performed and correlated
with the experimental DPPH• scavenging data. We used structural, geometrical,
topological and quantum-chemical descriptors selected with Genetic Algorithms in order to
determine which of these parameters are responsible of the observed DPPH• radical
scavenging activity. We constructed a back propagation neural network with the hydrophilic
factor (Hy) descriptor to generate an adequate architecture of neurons for the system
description. The mathematical model showed a multiple determination coefficient of 0.9196
and a root mean squared error of 0.0851. Our results shows that the presence of hydroxyl
groups on the ring structure of 3-carboxy-coumarins are correlated with the observed
DPPH• radical scavenging activity effects.

Molecules 2012, 17
Keywords: coumarins; QSAR; DPPH•; artificial neural networks
14883
1. Introduction
Antioxidants play important roles in preventing diseases induced by reactive oxygen species, which
result in oxidative damage, including protein denaturation, mutagenesis and degenerative or pathological
events, such as aging, asthma, and cancer. The diversity of structural characteristics in the natural and
synthetic coumarins offers a vast field of research for new biological properties of these compounds.
Coumarin derivatives constitute an important class of compounds with varied biological properties
such as anti-inflammatory, antibacterial, cytotoxic, anxiolytic, antitumor, anticoagulant, antiemetic and
antioxidant activity [1–7]. Due to thir widespread applications, biological activity evaluation of
coumarin derivatives has been a subject of intense investigations.
Here we proposed to measure the antioxidant activity in vitro assay of eight 3-carboxycoumarin
derivatives with different structural variations for modular replication by the quantitative 1,1-diphenyl-
2-picrylhydrazyl (DPPH•) radical scavenging activity method. These is the first time that this
measurement has been performed on these compounds, although a similar type of coumarins was
reported by Lin et al. in 2008 [8].
Quantitative Structure-Activity Relationship (QSAR/QSPR) methodologies are one of the most
powerful tools for describing the relationships between biological activity and the physicochemical
characteristics of molecules. Current literature demonstrates that almost every area of chemical and
life sciences, as well as technology, utilizes quantitative structure-activity/property relationships
(QSAR/QSPR) to accelerate product development and increase efficiency. The designs of
pharmaceuticals, agrochemicals, and consumer products as well as the assessment of their toxicity and
environmental impact have become major areas of application of QSAR/QSPR techniques, whose
methods also penetrate into relatively new applications such as materials science and nanotechnology.
In terms of methodology development the new trend is the integration of QSAR/QSPR with related
computational methods such as virtual screening and molecular dynamics. Such a synergy offers
unique opportunities and heralds a new era of computer-aided molecular design [9]. QSAR/QSPR
modeling usually consist of four main operations: calculating or measuring a pool of descriptors or
other input variables; choosing a small subset of these descriptors that are relevant to the biological
activity being modeled (in some cases this step may not be required); generating the often nonlinear
relationship between the descriptors and the global material property; and validating the model to
assess its reliability, robustness, predictivity, and domain of applicability [10]. Almost all QSPR
modeling methods involve some sort of regression. This can be simple least-squares, multiple linear
regression (MLR) or, where the structure-property relationship is not linear, a polynomial, bilinear, or
neural network method. The simplest QSPR modeling method is known as multiple linear regression,
It assumes that the property being modeled is a linear function of the descriptors [11]. To develop a
QSAR, a more significant number of compounds is required to develop a meaningful relationship. An
often asked question is “how many compounds are required to develop a QSAR?” There is no direct
and simple response to this question other than “as many as possible!” To provide some guide, it is

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widely accepted that between five and ten compounds are required for every descriptor in a QSAR [12].
This does suggest that a one descriptor regression-based QSAR could be developed on five
compounds. This is possible, but is very reliant on issues such as data distribution and range. Ideally
“many more” compounds are required to obtain statistically robust QSARs, with some modelling
techniques being considerably more data hungry than regression analysis. In our case, we have only
eight compounds whose biological activities have been determined experimentally in our laboratory.
Molecular descriptors are formal mathematical representations of a molecule, obtained by a
well-specified algorithm, and applied to a defined molecular representation or a well-specified
experimental procedure: the molecular descriptor is the final result of a logic and mathematical
procedure which transforms chemical information encoded within a symbolic representation of a
molecule into a useful number or the result of some standardized experiment. A general consideration
about the use of molecular descriptors in modeling problems concerns their information content. This
depends on the type of molecular representation used and the defined algorithm for their calculation.
There are simple molecular descriptors derived by counting some atom types or structural fragments in
the molecule, as well as physicochemical and bulk properties such as, for example, molecular weight,
number of hydrogen bond donors/acceptors, number of OH-groups, and so on. Other molecular
descriptors are derived from algorithms applied to a topological representation. These are usually
termed topological, or 2D-descriptors. Other molecular descriptors are derived from the spatial (x, y, z)
coordinates of the molecule, usually called geometrical, or 3D-descriptors; another class of molecular
descriptors, called 4D-descriptors, is derived from the interaction energies between the molecule,
imbedded into a grid, and some probe. Single indexes derived from a molecular graph are called
topological indexes. These are numerical quantifiers of molecular topology that are mathematically
derived in a direct and unambiguous manner from the structural graph of a molecule, usually an
H-depleted molecular graph. On the other hand many of those descriptors are based directly on the
results of quantum-mechanical calculations or can be derived from the electronic wave function or
electrostatic field of the molecule [13]. Since the electrophilicity index is a chemical reactivity
descriptor and its definition has strong foundation from the density functional theory [14,15], it is
appropriate to make use of this descriptor in the QSAR parlance. Recently the electrophilicity index
has been used as a possible descriptor of biological activity confirming the fact that the electrophilicity
properly quantifies the biological activity. Although there is no one-to-one agreement between AM1
and B3LYP values, the B3LYP method in general provides better estimates of biological activity when
compared to the corresponding AM1 values [15]. Within the density functional theory framework
some quantum chemical descriptors such the softness, chemical potential and electrophilicity index,
where used here because of the good correlation they have shown in the prediction of radical
scavenging antioxidant activity [16–19].
Genetic Algorithms (GA) are powerful computational tools that have been used in many areas of
investigation because of their reliable mathematical models. This method is based on the mechanism
of evolution of species, the higher descriptor weights (genes) the more preserved in the mathematic
model, while the lower weights are eliminated. In this manner, the best mathematical models which
represent the observed biological activity (phenotype) are obtained [20,21]. Furthermore, Artificial
Neural Networks (ANN) is a computational tool used in the rationale drug design. ANN tries to
simulate the human brain mechanism. In this method the basic unit is the neuron and the

Molecules 2012, 17
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interconnection of all of them forms the architecture of the neural network. There is a variation of this
method called back propagation ANN as well. In this, the output of the network is compared to the real
value and then the network weights are adjusted in order to ensure that the error is minimum. This type
of neural network is the most frequently used to develop of QSAR and QSPR studies [22,23].
2. Experimental Methods and Results
2.1. Synthesis and Characterization
Ethyl esters of 6-R-2H-1-benzopyran-2-one-3-carboxylic acids A1–D1 and 3-acetyl-6-R-2H-1-
benzopyran-2-ones A2–D2 (Scheme 1) were synthesized via Knoevenagel condensation. The general
reaction between 5-substituted salicylaldehydes A–D and ethyl acetoacetate or diethyl malonate at
refluxing temperature for 24 h gave moderate to good product yields [24–26]. The details and
spectroscopic data for those compounds are summarized in the Experimental procedures.
Scheme 1. Reaction for the formation of coumarins.
2.2. DPPH• Radical Scavenging Activity
Antioxidant compounds play an important role as a health-protecting factor. The interaction of the
examined compounds with the stable free radical DPPH• was studied. Results of the assays are
summarized in Figures 1–3.
Figure 1. DPPH• radical scavenging activity for 3-carboxycoumarin derivatives at a
10 mg/mL concentration reported as percent of inhibition of DPPH•.
100
90
80
70
60
50
40
30
20
10
0
A1
A2
B1
B2
C1
C2
D1
D2
Compounds at 10 mg/mL

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Compounds C1 and C2 showed the highest radical scavenging activity (Figure 1). For both
compounds the interaction was time and concentration dependent (Figures 2 and 3). The time course of
DPPH• interaction is affected by various concentrations. In general, this interaction expresses their
ability to scavenge free radicals [27,28]. Trials of discoloration of DPPH• at 60 min with different
concentrations of compounds C1 and C2 in order to verify the dose-effect of the concentration of these
compounds on the entrapment of the DPPH• radical [29] are shown below.
Figure 2. Kinetics of entrapment of DPPH• with respect to the concentration of the compound C1.
0,9000
0,8000
0.1 mg/mL
0,7000
1 mg/mL
2.5 mg/mL
5 mg/mL
0,6000
0,5000
0,4000
0,3000
0,2000
0,1000
0,0000
7.5 mg/mL
10 mg/mL
0
10
20
30
40
50
60
Time (min)
Figure 3. Kinetics of entrapment of DPPH• with respect to the concentration of the compound C2.
0,9000
0,8000
0,7000
0.1 mg/mL
0,6000
0,5000
0,4000
0,3000
0,2000
0,1000
0,0000
1 mg/mL
2.5 mg/mL
5 mg/mL
7.5 mg/mL
10 mg/mL
0
10
20
30
40
50
60
Tíme (min)
3. Computational Details and Results
A conformational study was performed over the eight coumarins (Table 1) using PM3 semi-empirical
method as implemented in the SPARTAN′08 code [30,31]. The structures of all conformers of
minimum energy were fully optimized without symmetry constrains within the density functional
theory methodologies and the resulting ground states were characterized via frequency analysis. In the
present work, we have used the hybrid B3LYP [32] functional and the 6-31+G (d,p) basis set [33]. We

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have included the influence of DMSO solvent using the SMD solvation model [34] implemented in the
Gaussian 09 program [35].
Table 1. Coumarin compounds and their experimental antiradical activity expressed as
equivalents in % DPPH• inhibition.
Compound
R2
R1
H
NO2
OH
OCH3
H
NO2
OH
% DPPH• inhibition
Log Yexp
1.13
A1
B1
C1
D1
A2
B2
C2
D2
COOCH3
COOCH3
COOCH3
COOCH3
COOEt
COOEt
COOEt
COOEt
13.42
14.43
40.67
9.39
8.06
8.10
1.16
1.61
0.97
0.91
0.91
0.75
1.06
56.39
11.41
OCH3
Molecular descriptors of all optimized structures were calculated from the DFT context and the
DRAGON´05 program [36]. This software includes 20 families of descriptors in the code. Here, we
have selected group account, geometrical and molecular property families. These families include a
total of 257 descriptors but DRAGON program only gave us 73 descriptors based on the molecular
characteristics of our compounds. We calculated the correlation matrix of these 73 descriptors the data
analyzer within the Molegro Virtual Docker (MVD) software [37] and obtained nine non-correlated
descriptors (see Table 2).
Table 2. Descriptors used in our study.
PROGRAM
DRAGON
DRAGON
DRAGON
DRAGON
DESCRIPTOR
TYPE
Geometrical
Molecular Properties
Molecular Properties
Molecular Properties
DESCRIPTION
Spherosity
Unsaturation index
Hydrophilic factor
Molar refractivity
SPH
Ui
Hy
AMR
Ghose-Crippen-Viswanadhan
octanol-water partition coefficient
Topological Polar Surface Area
Total Energy
DRAGON
ALOGP
Molecular Properties
DRAGON
GAUSSIAN
GAUSSIAN
GAUSSIAN
GAUSSIAN
GAUSSIAN
GAUSSIAN
GAUSSIAN
TPSA
Molecular Properties
Quantum-Chemical
Quantum-Chemical
Quantum-Chemical
Quantum-Chemical
Quantum-Chemical
Quantum-Chemical
Quantum-Chemical
E
Dipole
Dipole Moment
Hardness
Electrophilicity
η
ω
µ
S
Chemical potential
Softness
Energy difference LUMO-HOMO
Gap HOMO-LUMO
The SPH (spherosity) is an anisometry descriptor calculated as a function of the eigenvalues of the
covariance matrix calculated from the molecular matrix:
(1)
The spherosity index varies from zero for flat molecules, such as benzene, to one for totally
spherical molecules [38]. The Ui (unsaturation index) is a simple information index for unsatured bonds
defined as:

Molecules 2012, 17
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(2)
where nDB, nTB and nAB are the number of of double, triple and aromatic bonds, respectively[36].
The Hy is the hydrophilic factor descriptor and it’s calculated from Equation (3):
(3)
where
is the number of hydrophilic groups (-OH, -SH and -NH2), nC represents the number of
carbon atoms and nSK stands for all atoms excluding Hydrogen [39]. The AMR (molar refactivity)
descriptor is calculated according to the Ghose-Crippen model, based on a group contribution
method [40]. The ALOGP descriptor (Ghose-Crippen-Viswanadhan octanol-water partition coefficient)
is calculated from the ALOGP model consisting of a regression equation based on the hydrophobicity
contribution of 120 atom types [41]. The TPSA (Topological Polar Surface Area) descriptor originally
proposed by Ertl P. et al. [42] is calculated from Equation (4):
(4)
the Ci term is the contribution of atom i to the molecular surface, ni is the frequency of the atom i in
the molecule and the sum runs over all types of polar fragments. The TPSA calculation takes into
account the contribution of the functional groups containing oxygen and nitrogen atoms to the
polarization of the molecular surface as implemented in the DRAGON code [36].
Additionally we calculated quantum chemical descriptors from DFT (Table 2) as total energy (E),
dipole moment, hardness (η), electrophilicity index (ω), chemical potential (µ), softness (S) and gap
HOMO-LUMO. In this work E, corresponds to the ground state energy of our coumarin molecules and
the dipole moment was calculated as implemented in Gaussian 09 [35]. The chemical potential (µ),
which is widely used as a descriptor of chemical reactivity, indicates the escape tendency of the
electrons and it’s calculated from:
(5)
where E is the energy of the system and N is the number of electrons [14]. Here we used the finite
difference approximation:
(6)
where I is the vertical ionization potential defined as the difference of total energy between cationic
structures in the optimized geometry of the neutral compounds and the optimized neutral structures:
I = Ecat − Eneu
(7)
A is the vertical electron affinity defined as the difference of the total energy between the optimized
neutral structures and the corresponding anions in the optimized geometry of the neutral compounds:
A = Eneu − Eanion
(8)
The hardness (η) is a global property of the molecular system and measure the resistance imposed
by it to any change in its electron distribution:

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(9)
In the finite difference approximation the above equation is:
(10)
The softness (S) is the inverse of hardness:
(11)
The electrophilicity index (ω) can be determined from chemical potential ( ) and hardness ( ) [14] as:
(12)
where ω represents the stabilization energy of the molecular system when it is saturated by electrons
coming from the surroundings [43].
3.1. Genetic Algorithms (GA)
We introduced all 13 descriptors into the Neuroshell Predictor program code [44]. According to the
GeneHunter Genetic Algorithm [45] implemented in this program we obtained the weights of the
molecular descriptors (see Figure 4).
Figure 4. Descriptor weights calculated from GA analysis.
Figure 5 shows the linear correlation between the log Yexp (actual) and log Ypred calculated by GA
analysis (predicted). We obtained a coefficient of multiple determinations (Rsquared) of 0.9313, a
correlation factor (r) of 0.9658 and a root mean squared error (RMSE) of 0.0786.
Rsquared is a statistical indicator usually used in multiple regression analysis to compare the
reliability of the model with respect to reference points. Rsquared is defined as:
(13)
where y is the experimental value, is the value predicted by the model, is the average of all the
output values. Furthermore r is a measure of the linear correlation between experimental and predicted
values in terms of direction, namely:

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(14)
Figure 5. Calculated against experimental antiradical activity.
RMSE is defined as the root mean square of the summation of quadratic terms. These terms
correspond to the difference between experimental and predicted data values:
(15)
Experimental and calculated antiradical activity, error and percent error are shown in Table 3. The
error is calculated from the difference between experimental (Yexp) and calculated (Ycal) antiradical
activity. Percent error is calculated as:
(16)
Table 3. log Yexp and log Ypred antiradical activity, error and % error values of coumarin compounds.
Compound
Log Yexp
1.13
Log Ypred
1.16
Error
−0.03
0.03
% Error
2.65
A1
B1
C1
D1
A2
B2
C2
D2
1.16
1.13
2.58
1.61
1.75
−0.14
−0.08
0.00
8.69
0.97
1.05
8.24
0.91
0.91
0.00
0.91
0.91
0.00
0.00
1.75
1.61
0.14
8.00
1.06
1.06
0.00
0.00

Molecules 2012, 17
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The highest error value was 8.69% and the lowest one 0%. The average percent error was 3.77%.
We propose the construction of a Back Propagation Neural Network (BPNN) with the most important
descriptor Hy (Table 4) in order to obtain a mathematical model that fits with the QSAR theory, this is
one descriptor per 4 to 10 molecules.
Table 4. Hydrophilic Factor (Hy).
Compound
Hy
A1
B1
C1
D1
A2
B2
C2
D2
−0.766
−0.634
−0.200
−0.734
−0.734
−0.621
−0.198
−0.709
3.2. Backpropagation Neural Network
NeuroShell Predictor software [44] was used to build and train our BPNN. The BPNN framework
was formed with one input neurons, five hidden neurons and one output neuron (see Figure 6).
Figure 6. Architecture of the back propagation neural network. Ellipses correspond to
neurons and lines represent the interconnection between them. (1) Input layer, (2) Hidden
layer and (3) Output layer.
(1)
(2)
(3)
The BPNN model showed that in all the analyzed compounds Hy descriptor is the most important
variable in the antiradical activity. The Hy descriptor indicates antiradical activity increases as we
incorporate hydrophilic groups to the coumarin molecules.
The linear correlation between log Yexp and log Ypred antiradical activity of coumarins was very
successful. The graphic is showed in Figure 7. Here we obtained a Rsquered of 0.9196, r = 0.959 and
RMSE = 0.0850.
Experimental and calculated antiradical activity, error and percent error are shown in Table 5. The
highest % error value was 14.29% and the lowest one 1.26%. In our opinion the high errors should
decrease as the number of molecules is increased. In the BPNN methodology the average percent error
was 7.18% which corresponds to a 3.41% higher than the calculated from GA.
Determination of reliability of our QSAR model was done by calculating the statistical parameters
and ∆ proposed by Roy et al. [46,47]. The
value for this mathematical model was 0.8687 and

Molecules 2012, 17
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the ∆ = 0.0759. For an acceptable QSAR model the average rm2 must be >0.5 and ∆rm2 < 0.2, in this
2
terms the QSAR model proposed here was good. In contrast
and ∆rm values for our GA model was
of 0.9014 and 0.056 respectively, but we have to consider that in GA analysis 13 descriptors were used
and in the ANN only one. These results show the importance to include ANN with the GA
methodology. A previous QSAR study [48] made with Multiple Linear Regression and 15 more
complex coumarins derivatives they found that the HOMO, LUMO and partial charges in the OH, N
and S where the most important descriptors for the development of the antiradical scavenging activity.
There’s results concord with ours in the way that Hy take account the functional groups OH, NH2 and
SH. Also in our study we validated our model with the statistical parameters
are a rigorous method for QSAR evaluation.
and ∆ [44,45] that
Figure 7. Calculated against experimental antiradical activity.
Table 5. Experimental and calculated antiradical activity, error and % error values of
coumarin compounds with BPNN model.
Compound
Log Yexp
1.13
Log Ypred
1.03
Error
0.1
% Error
8.85
1
B1
C1
D1
A2
B2
C2
D2
1.16
1.04
0.12
10.34
1.86
1.61
1.64
−0.03
−0.04
−0.1
−0.13
0.04
0.97
1.01
4.12
0.91
1.01
10.99
14.29
2.29
0.91
1.04
1.75
1.71
1.06
1.01
0.05
4.72

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It’s important to mention that the C1 and C2 compounds show the highest antiradical activities
because both possess an -OH hydrophilic group. This functional group increases the Hy value in such
a way that we could say that -OH group is crucial for antiradical activity of coumarins.
4. Experimental
4.1. General
All chemicals and solvents were of reagent grade and used as received. Melting points were
measured on an Electrothermal IA 9100 apparatus and were uncorrected. IR spectra were recorded
neat using a Varian 3100 FT-IR with ATR system Excalibur Series spectrophotometer. Mass spectra
1
were obtained in a Bruker Esquire 6000 spectrometer with an electron ionization mode. H and
13C-NMR spectra were recorded on a Varian Mercury 300 (1H, 300.08; 13C, 75.46 MHz) instrument in
CDCl3 solutions or DMSO-d6, measured with SiMe4 as the internal reference, chemical shifts are in
ppm and nJ(H-H) in Hertz.
General Procedure for the Synthesis of Coumarin Derivatives
The starting coumarins 1A–2D were synthesized by Knovenagel cyclization (Scheme 1) between
substituted salicylaldehydes (4 mmol) and ethyl acetoacetate (4 mmol) or diethyl malonate (4 mmol)
with catalytic amounts of piperidine in ethanol (20 mL), according to the methodology reported
elsewhere [24–26].
3-Acetyl-2H-1-benzopyran-2-one (A1): Yellow solid. Yield 67.3%. m.p: 118–122 °C, IR (neat),(cm−1):
1
1719 (OC=O), 1680 (C=O), 1196 and 1161 (C-O). H-NMR ( ppm, CDCl3): 8.63 (s, 1H, H-4), 7.92
3
3
3
(d, 1H, H-5, J = 7.7 Hz), 7.72 (dd, 1H, H-7, J = 7.6, 7.3 Hz), 7.43 (dd, 1H, H-8, J = 7.3 Hz), 7.39
(dd, 1H, H-6, 3J = 7.7, 7.6 Hz), 2.56 (s, 3H, H-12). 13C-NMR (ppm, CDCl3): 195.1 (C-11), 158.5 (C-2),
154.6 (C-9), 147.1 (C-4), 134.5 (C-7), 130.8 (C-5), 124.9 (C-6), 124.4 (C-3), 118.2 (C-10), 116.1 (C-8),
30.1 (C-12). C11H8O3 188.05. m/z = 188.1 (M, 50%), 173.2 (100%), 145.3 (11.9%), 118.3 (11.1%).
3-Acetyl-6-nitro-2H-1-benzopyran-2-one (B1): Yellow solid. Yield 54.6%. m.p: 200–203 °C; IR (neat),
1
(cm−1): 1745 (OC=O), 1676 (C=O), 1530 and 1341 (C-NO2), 1275 y 1209 (C-O). H-NMR ( ppm,
CDCl3): 8.58 (d, 1H, H-5, 4J = 2.6 Hz); 8.55 (s, 1H, H-4); 8.50 (dd, 1H, H-7, 3J = 9.1, 4J = 2.6 Hz), 7.52 (d,
1H, H-8, 3J = 9.1 Hz), 2.73 (s, 3H, H-12). 13C-NMR ( ppm, CDCl3): 194.5 (C-11), 158.6 (C-2), 157.9
(C-9), 146.1 (C-4), 144.6 (C-6), 128.8 (C-7), 126.5 (C-3), 126.1 (C-5), 118.4 (C-10), 118.2 (C-8), 30.7
(C-12). C11H7NO5 233.03. m/z = 233.0 (M, 43.9%), 218.3 (100%), 172.3 (19.3%), 144.2 (5.0%).
3-Acetyl-6-hydroxy-2H-1-benzopyran-2-one (C1): Yellow solid. Yield 90%. m.p: 247–248 °C; IR
1
(neat), (cm−1): 3155 (O-H), 1736 (OC=O), 1643 (C=O). H-NMR( ppm, DMSOd6): 9.89 (s, 1H,
OH), 8.53 (s, 1H, H-4), 7.20 (d, 1H, H-5, 4J = 2.9 Hz); 7.14 (dd, 1H, H-7, 3J = 8.8, 4J = 2.9 Hz), 7.29
(d, 1H, H-8, 3J = 8.8 Hz), 2.54 (s, 3H, H-12). 13C-NMR ( ppm, DMSO-d6): 196.0 (C-11), 159.9 (C-2),
154.6 (C-6), 148.7 (C-9), 147.7 (C-4), 125.1 (C-3), 123.4 (C-7), 119.3 (C-10), 117.2 (C-5), 114.8 (C-8),
30.7 (C-12). C11H8O4 204.04. m/z = 204.1 (M, 86.0%), 189.1 (100%), 161.2 (19.0%), 134.2 (33.4%).

Molecules 2012, 17
14894
3-Acetyl-6-methoxy-2H-1-benzopyran-2-one (D1): Yellow solid. Yield 89%. mp 180–183 °C. IR
(cm−1): 1723 (OC=O), 1677 (C=O), 1226, 1197 (C-O).1H-NMR (ppm, CDCl3): 8.44 (s, 1H, H4),
3
3
4
4
7.28 (d, H-8, 1H, J = 9.1,), 7.20 (dd, 1H, H-7, J = 9.1, J = 2.9 Hz), 7.02 (d, 1H, H-5, J = 2.6 Hz),
13
3.85 (s, 3H, OCH3), 2.70 (s, 3H, CH3); C-NMR ( ppm, CDCl3): 195.9 (C11), 159.7 (C2), 156.6
(C6), 150.1 (C10), 147.6 (C4), 124.8 (C3), 123.2 (C7), 117.9 (C5), 118.7 (C9), 111.3 (C8), 56.1
(OCH3), 30.9 (CH3). C12H10O4 218.06 m/z = 218.0 (M, 60.0%), 203.0 (100%), 175.0 (16.6%), 148.0
(15.3%). EA (%) calculated for C12H10O4: 66.05 C, 4.62 H; found: 66.04 C, 4.61 H.
Ethyl 2H-1-benzopyran-2-one-3-carboxyate (A2): White solid. Yield 90%. m.p. 91–92 °C, IR (neat),
(cm−1): 1605 (OC=O), 11758 (C=O), 1196 y 1161 (C-O). 1H-NMR ( ppm, DMSO-d6): 8.49 (s, 1H,
H-4), 7.93 (dd, 1H, H-5), 7.39 (dt, 1H, H-6), 7.74 (dt, 1H, H-7), 7.45 (dd, 1H, H-8), 4.30 (q, 2H,
13
O-CH2), 1.39 (t, 3H, -CH3). C-NMR ( ppm, DMSO-d6): 163.0 (C-11), 156.4 (C-2), 154.5 (C-9),
149.0 (C-4), 134.7 (C-7), 130.7 (C-5), 125.3 (C-6), 118.2 (C-3), 118.3 (C-10), 116.6 (C-8), 61.7 (O-CH2),
15.5 (-CH3). C12H10O4 218.06. m/z = 219.1 (M, 100%), 173.2 (76%), 146.2 (87.9%), 118.2 (27.4%).
Ethyl 6-nitro-2H-1-benzopyran-2-one-3-carboxyate (B2): Yellow solid. Yield 89%. m.p = 191–192 °C,
IR (neat), (cm−1): O-C=O (1716), C=O (1746). 1H-NMR( ppm, CDCl3): 8.90 (d, 1H, H-5, 4J = 3.0 Hz);
8.93 (s, 1H, H-4); 8.50 (dd, 1H, H-7, 3J = 9.0, 4J = 3.0 Hz), 7.65 (d, 1H, H-8, 3J = 9.0 Hz), 4.32 (q, 2H,
O-CH2), 1.33 (t, 3H, -CH3). 13C-NMR ( ppm, CDCl3): 162.5 (C-11), 158.5 (C-2), 155.2 (C-9), 147.1
(C-4), 148.8 (C-6), 128.8 (C-7), 118.0 (C-3), 125.4 (C-5), 120.7 (C-10), 118.3 (C-8), 62.7 (O-CH2),
14.3 (-CH3). C12H9NO6 263.04. m/z = 262.9 (M, 33.0%), 218.2 (91.9%), 191.0 (100%), 161.2 (98.0%).
Ethyl 6-hydroxy-2H-1-benzopyran-2-one-3-carboxyate (C2): Beige solid, 91%, m.p. = 205–206 °C, IR
1
(neat,) (cm−1): (O-H) 3324, (O-C=O) 1707, (C=O) 1722. H-NMR ( ppm, DMSO-d6): 9.93 (s, 1H,
3
4
OH), 8.66 (s, 1H, H4), 7.30 (d, 1H, H-8, J = 8.8), 7.21 (d, 1H, H-5, J = 2.9), 7.17 (dd, 1H, H-7,
3J = 8.8, J = 2.9 Hz), 4.29 (q, 2H, OCH2-, J = 7.1), 1.31 (t, 3H, -CH3, J = 7.1), C-RMN ( ppm,
DMSO-d6): 167.4 (C-11), 159.4 (C-2), 154.5 (C-9), 147.2 (C-4), 140.9 (C-6), 117.1 (C-7), 119.4 (C-3),
113.3 (C-5), 119.7 (C-10), 120.2 (C-8), 44.1 (O-CH2), 14.2 (-CH3). C12H10O5 234.05. m/z = 234.0 (M,
67.7%), 189.1 (36.1%), 161.8 (100.0%), 134.2 (31.3%)
4
3
3
13
Ethyl 6-methoxy-2H-1-benzopyran-2-one-3-carboxyate (D2): Yellow solid, 92%, m.p. = 142–143 °C,
1
IR (neat), (cm−1): (O-C=O)1733, (C=O)1740. H-NMR ( ppm, CDCl3): 8.45 (s, 1H, H-4), 7.26
3
4
(d, H-8, 1H, J = 5.0), 7.20 (dd, 1H, H-7, 3J = 5.0, J = 2.9, Hz), 6.99 (d, 1H, H-5, 4J = 2.9 Hz), 3.89
3
3
13
(s, 3H, OCH3) 4.38 (q, H, OCH2, J = 7.0, Hz), 1.47 (s, 3H, -CH3, J = 7.0 Hz); C-NMR ( ppm,
CDCl3): 163.3 (C-11), 157.1 (C-2), 156.4 (C-9), 148.6 (C-4), 149.9 (C-6), 118.0 (C-7), 118.3 (C-3),
110.8 (C-5), 118.6 (C-10), 122.8 (C-8), 62.1 (O-CH2), 56.1 (CH3O-), 14.4 (-CH3). C13H12O5 248.07.
m/z = 248.2 (M, 93.1%), 203.1 (32.6%), 176.2 (100%), 148.2 (23.7%).
4.2. Antiradical Activity Measurement with the DPPH• Assay
The antiradical activity of compounds A1–D2 was estimated according to a slight modification of
the procedure reported by Morales and Jimenez-Perez [27]. Dilutions in DMSO solvent at 10 mg/mL
of the eight compounds were prepared. An aliquot of each sample (50 μL) was added to a solution of

Molecules 2012, 17
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1,1-diphenyl-2-picrylhydrazyl (DPPH•) radical (250 µL) prepared fresh daily, at a concentration of
74 mg/L in ethanol. The mixtures (200 µL) were placed in a 96-well microplate and absorbance at time
zero was immediately measured using a UV wavelength of 520 nm. Measurement were performed
every 5 min for 60 min. Antiradical activity evaluation for compounds was measured in terms of
absorbance decrease at 520 nm of the DPPH• ethanolic solution produced by the effect of each
compound as a result of their ability to donate a hydrogen giving place to the reduced form of DPPH•.
6-hydroxy-2,5,7,8-tetramethylchroman-2-carboxylic acid (Trolox) was used as standard molecule. The
antiradical activity for each compound was determinate in Trolox equivalent antioxidant capacity
(TEAC). The DPPH• solution in presence of DMSO and in the absence of coumarins was tested and
used as a negative control. A null DPPH• free radical scavenging for the DMSO was verified. In all
experiments, samples were analyzed in triplicate, and mean values ± SD were recorded in order to
present the activity for each compound and be able to evaluate the structure-activity relationships.
5. Conclusions
In GA analysis we obtained an average percent error of 3.77% while in BPNN the average percent
error was 7.18%. This result indicate that the combination of the two methodologies optimize the
creation of QSAR models. The GA allows finding the most important descriptor for the development
of the antiradical activity and ANN improves our model with the use of only one molecular descriptor
to obtain accurate prediction values. The presence of hydroxyl groups on the ring structure of
3-carboxycoumarins is correlated with their DPPH• radical scavenging effects. The mixed QSAR
model showed that Hy could indicate that antiradical activity would increase as we incorporate
hydroxyl groups in the coumarin molecules. According to
and ∆
obtained for ANN the
mathematical model proposed in this work has good predictive ability.
Acknowledgments
The authors are grateful for financial support from Conacyt (Grants 105721 and 173591), FRABA
Universidad de Colima (Grant 740/11 and 797/2). RSRH is greatfully acknowledged to Conacyt for
the grant number 33990 for PhD formation.
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Sample Availability: Samples of the compounds A1–D2 are available from the authors.
© 2012 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article
distributed under the terms and conditions of the Creative Commons Attribution license
(http://creativecommons.org/licenses/by/3.0/).