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QSAR of Antioxidants
Based on the best QSAR model, the authors have designed four new antioxidants, which have a very
high radical scavenging ability. So it can be expected that the result of this study should not only help
in design of new antioxidants which will prevent free radicals from acting but also facilitate the QSAR
study of π-system stabilization in radical form. In conclusion, the excellent QSAR results for flavonoids
were obtained using important quantum chemical descriptors which were calculated based on DFT
method. From the analysis of the QSAR equation, it can be concluded that in order to get better radical
scavenging activity, the hardness, group electrophilic frontier electron density and group electrophilicity
should be decreased. In other words, electron-donating capacity and electron-accepting capacity should
be reduced. This descriptor explains the contradictory results regarding the antioxidant activities of
flavonoids. It depends not on any individual substituents but the effect of the substituents as a whole on
E
the planar ring via the (F
) descriptor.
A
The researchers (Rasulev, Abdullaev, Syrov & Leszczynski, 2005) have tried to perform a QSAR
study on the antioxidant activity of flavonoids. This QSAR study has been carried out for 27 flavonoids
belonging to four different groups (isoflavons, flavons, flavonols, flavanons) to correlate and predict the
inhibition of lipid peroxidation effects (antioxidant activity). The Genetic Algorithm (GA) was used to
select the descriptors and Multiple Linear Regression analysis (MLRA) was used to generate the correlation models that relate the structural features (descriptors) to the biological activities. The obtained
equations consist of one to four descriptors calculated from the characteristics of the molecular structures with use of DRAGON software and quantum-chemical methods. A number of quantum chemical
molecular descriptors were obtained from the density functional theory (DFT) B3LYP/6-31G(d, p) level
optimized geometries. The results of the GA-MLRA analysis show that the position of the OH groups,
the magnitude of dipole moment and the shape of the molecule play an important role in inhibition of
lipids peroxidation by flavonoids. The significant QSAR models were obtained with r-values of 0.935
2
and 0.933 for basic models. The q
(cross validation r2) values and scrambling/randomization experiments
also confirm the statistical significance of the proposed models. These models are expected to be useful
for screening of flavonoid antioxidants. From the best QSAR model, one of the important electronic
molecular properties is the dipole moment, which is related to the interaction between a drug and the
target molecule. The value of dipole moment defines the orientation of the flavonoid and the interaction
rate, which are important in driving the interaction. Data analysis indicates that a good inhibitor of lipid
peroxidation should have a small dipole moment (no more then 5 – 6 Debye).
The best QSAR models were built based on μ, I
Glc
and I
descriptors, where
OH .
• I
• I
: Inhibition of lipids peroxidation.
LPO
: Indicator descriptor, number of OH groups at positions C-3 (C - ring) and the 3’,4’ position
OH
in the B-ring.
• μ: Dipole moment.
• I
: Indicator descriptor, number of Glycoside-like fragments.
Glc
In conclusion, this work has shown that a set of specific parameters define the antioxidant activity of
flavonoids. One of the main parameters is the indicator variable which embraces the OH groups of the
flavonoid core. Other parameters are the quantum-chemical descriptor and the topological descriptor
that make it possible to build reliable QSARs. The present analysis of flavonoid QSARs offers insight
into their possible mechanisms of action and could be used in the design of new flavonoid-based drugs
for the handling of free radical-mediated disease conditions.
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QSAR of Antioxidants
In 2011 (Hemmateenejad, Mehdipour, Deeb, Sanchooli & Miri,) have done a QSAR study related to
variable selection in Counter Propagation Neural Networks (CPNN) as a classification tool, with an application to 105 flavonoid compounds of known inhibitory activity against protein tyrosine kinase using
substituent electronic descriptors (SED) as novel source of electronic descriptors. In calculating the SED
descriptors, the substituents were considered as radical molecules. The molecular structures of all the
substituent radical structures were built and full geometry optimization was done for them. Considering
no molecular symmetry constraint, all bond lengths and angle optimization were carried out at the level
of UHF/6-31G and RHF/6-31G. The calculated descriptors can be classified into three different electronic categories including local charges, dipoles, and orbital energies. The quantum chemical indices of
hardness (HD), softness (SOF), electronegativity (EN), and electrophylicity (EPH) were also calculated.
Some additional descriptors such as HOMOA (Highest occupied molecular orbital energy), HOMOB,
LUMOA (Lowest unoccupied molecular orbital energy), LUMOB, HDB, SOFA, SOFB, ENA, ENB,
EPHA, and EPHB stem from two different alpha and beta electronic population energy were calculated
where the subscript A and B stand for alpha and beta population of electronic energy, respectively.
Therefore, a total of 25 electronic descriptors were calculated for each substituent. Since the studied
flavonoid derivatives pose 8 substitution positions, for each molecule a data matrix of SED parameters
is obtained by placing the row vectors of different substituents under each other. Consequently, a 3- way
data array is obtained by collecting the SED data matrices of different molecules beside each other’s.
The Classification is a useful tool in pattern recognition of the data set with discrete activity (i.e. active
and non-active). Among the classifier methods, Counter Propagation Neural Network has attracted a lot of
attention because of its ability to model both discrete and continuous activities. However, feature selection
remains a problem. In this study, some filter, wrapper and combinatorial approaches of feature selection
and feature extraction were evaluated in order to find optimal procedure for CPNN in a classification
problem. Results of all methods showed that F score-based ranking demonstrated the best result while
combination of feature extraction method, Principal Component Analysis (PCA) with feature selection
process gave poorer results compared to using feature selection alone. In addition, filter methods were
better than wrapper methods in this study, since they showed higher performance and also more rational
trend in PC-CPNN. Moreover, 3-way handling of data, Parallel Factor Analysis (PARAFAC) gave the
worst result which may be due to loss of informative data during transformation process and restricted
rotational freedom in calculating the scores. Comparison of CPNN with linear discriminant analysis
(LDA), as a routine method of classification in QSAR studies, revealed that applying feature selection
to CPNN resulted in better predictions compared to LDA. In conclusion, Counter Propagation Neural
Network (CPNN) is one of the most attractive classification tools for QSAR studies. The performance of
some different feature selection algorithms was evaluated in order to find the best classification model.
The methods were applied for modeling protein-tyrosine kinase inhibitory of 105 flavonoid derivatives
using substituent electronic descriptors. Applying feature selection to CPNN resulted in better prediction.
Hydroxyphenylureas
In 2008 (Deeb, Youssef & Hemmateenejad,) performed a QSAR analysis of novel hydroxyphenylureas
as antioxidant agents. A set of descriptors consisting of quantum, chemical, and topological descriptors
had been used for modeling the activity of 36 novel hydroxyphenylureas to scavenge Free Radicals (FRs)
produced by Peripheral Multinuclear Neutrophil Cells (PMNs) expressed as the rate of change per unit
added volume of FR (K
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min_1). Simple, as well as multiple regressions have indicated that PW2 (Path\
app
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QSAR of Antioxidants
Walk-2-Randic Shape Index) is the most dominating parameter to be used in modeling K
. Excellent
app
results were obtained in multiparametric regressions. The results of this study are critically discussed
using a variety of statistical parameters. The models are validated using Leave-One-Out (LOO) and
Leave-Group-Out (LGO) cross validation, external test set, and chance correlation. The general structure
of hydroxyphenylureas is given in Figure 2.
The best QSAR model obtained in this study was built using MPC, SSC, S0K, DM
and DM
molecular descriptors where:
t
z, Qmean,
X5, PW2
• MPC: Most positive charges,
• Qmean: The Mean absolute charge (charge polarization),
• SSC: Sum of squares of charges,
• X5: Connectivity index Chi-5,
• S0K: Kier symmetry index,
• PW2: Path/walk-2-randic shape index,
• DMz: Molecular dipole moment at z-direction,
• DM
: Total molecular dipole moment.
t
In conclusion, the quantum and topological descriptors can be successfully used for modeling the activ-
ity of novel hydroxyphenylureas to scavenge FRs produced by PMNs, expressed as the rate of change per
-1
unit added volume of FR Kapp min
. PW2 is the most dominating parameter to be used in modeling K
app
.
Curcumin Analogues
In 2014 (Chen, Zhu, Chen, Dong & Li,) performed 3D-QSAR study on the antioxidant capacity or curcumin analogues. A comparative molecular similarity indices analysis (CoMSIA) was done on a set of
27 curcumin-like diarylpentanoid analogues and their DPPH scavenging activities. This study indicates
that a combination of steric, hydrophobic, hydrogen bond donor and hydrogen bond acceptor fields,
show good correlative and predictive properties. Moreover, the authors have been able to specifically
derive chemical properties that are important to activity, and hence adopt a rational approach toward
the selection of substituents at various positions in our scaffold; favored and disfavoured regions for
enhanced antioxidative activity were suggested. A significant cross-validated correlation coefficient Q
= 0.784, SEP = 0.042 for CoMSIA was obtained, indicating the statistical significance of the correlation scaffold. The results have been used as a guide to design compounds that, potentially, have better
activity against oxidative damage. Figure 3 shows the structure of the curcumin.
Figure 2. General structure of hydroxyphenylureas
2
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QSAR of Antioxidants
Figure 3. Structure of the curcumin
Hydroxybenzalacetones
In 2005 (Yamagami, Akamatsu, Motohashi, Hamadaa & Tanahashi,) performed a QSAR study for antioxidant hydroxybenzalacetones by quantum chemical and 3-D-QSAR (CoMFA) analysis. Antioxidant
activities for a series of hydroxybenzalacetones, OH-BZ, evaluated by their inhibitory potencies against
lipid peroxidation induced by c-ray irradiation or t-BuOOH, were analyzed quantitatively using quantumchemical parameters calculated by semi-empirical molecular orbital calculations. The energy of the highest
occupied molecular orbital (E
together with the steric parameter (E
) and frontier electron densities on the phenolic oxygen atom (F
HOMO
) for the substituent ortho to the phenolic oxygen, showed excellent
s
correlations. The classical QSAR approach and the approach in this study led the authors to conclude that
the activity is increased by electron-donating substituents and/or by bulky ortho substituents: the former
is likely to facilitate the hydrogen abstraction from phenols and the latter is considered to function to
stabilize the resultant phenoxy radical. Although classical QSAR using various experimentally derived
physicochemical parameters affords more straightforward information about the reaction mechanism,
the successful prediction of activity by quantum chemical QSARs in terms of parameters obtained by
rapid semiempirical molecular orbital calculations will permit their wider application in the search for
antioxidant compounds. The authors also performed 3D-QSAR studies by using the comparative molecular field analysis (CoMFA) model.
In conclusion, the best QSAR model was obtained in this study by using quantum chemical descriptors such as HOMO, ΣF H,O and ΣEs as well as indicator variable (Ip).
The definition of these variables in the QSAR model is given in the above text. This result demonstrates
that the higher the HOMO energy and frontier electron density (HOMO) on phenolic O-atom(s), and
the bulkier ortho substituent(s), the greater the antioxidant activity is. Since the former two factors are
related to the facility of electrondonating ability of substituents, the QSARs obtained here are considered
to have a physical meaning comparable to that of the previous classical QSARs.
H,O
),
Phenolic Derivatives Bearing No Donor Groups
In 2011 (Mitra, Saha & Roy,) performed a QSAR study of 33 antioxidant compounds belonging to the
class of phenolic derivatives bearing NO donor groups. This dataset exhibiting a wide range of antioxidant activity (IC
(Thiobarbituric acid reactive substance) assay method. Different types of descriptors were calculated
in this study such as topological indices, structural, thermodynamic, spatial and electronic descriptors.
The chemometric tools used in this study were the Genetic Function Approximation (GFA) and Genetic
Partial Least Squares (G/PLS) techniques. Initially, QSAR model was developed using the entire data
set using stepwise regression. In order to determine the external predictive ability of models, the data
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). The antioxidant activities of the compounds were measured using the TBARS
50
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QSAR of Antioxidants
set was divided into training (25 compounds) and test (8 compounds) sets using the k-means clustering
technique and external validation was done based on the activity prediction of the test set compounds.
2
= 0.917 (for the test set) indicates significant ability of the developed model to predict the activity
R
pred
of new compounds belonging to this series of phenolic derivatives. A randomization test was performed,
and the results indicate that the developed models are sufficiently robust and not the outcome of mere
chance. In conclusion, the present work deals with QSAR studies of a series of antioxidants belonging
to the class of phenolic derivatives bearing NO donor groups. Several QSAR models with appreciable
statistical significance have been reported. Models were built using various chemometric tools, which
were validated both internally and externally. These models chiefly infer that presence of substituted
aromatic carbons, long chain branched substituents, an oxadiazole-N-oxide ring with an electronegative
atom containing group and high degree of methyl substitutions of the parent moiety are conducive to the
antioxidant activity profile of these molecules. The novelty of this study is not only that the structural
attributes of NO donor phenolic compounds required for potent antioxidant activity have been explored,
but 15 new compounds with possible antioxidant activity have also been designed and their antioxidant
activity has been predicted in silico.
Coumarin Derivatives
Recently (Mitra, Saha & Roy, 2013) performed predictive modeling of antioxidant coumarin derivatives using different approaches that include: Descriptor based QSAR, 3 dimensional pharmacophore
mapping and Hologram Quantitative Structure-Activity Relationship (HQSAR). The study deals with
a series of coumarin derivatives (45 compounds) that were modeled for their antioxidant activity based
on their ability to inhibit DPPH (1,1-diphenyl-2-picryl-hyrazyl) free radicals. The descriptor-based
QSAR model provides quantitative insight about the different molecular pre-requisites for exhibiting
potential antioxidant activity. The three dimensional pharmacophore model and the HQSAR model
enable the detection of the different molecular features and the atomic fragments that are important
to the antioxidant activity of the coumarin derivatives. Figure 4 shows the parent coumarin nucleus
used in this study:
The three different types of QSAR models developed in this study simplify the process of identifying the essential molecular fragments and in that way facilitate the selection of molecules exhibiting
improved radical scavenging activity. The descriptor-based QSAR model was developed using two
different chemometric tools which include the Genetic Function Approximation (GFA) and Genetic
Partial Least Squares (G/PLS) techniques based on both linear and spline options. Based on the different internal and external validation metrics, the G/PLS model developed using the spline option
was selected as the best one. The model infers that compounds bearing a higher degree of branching,
which then leads to a decrease in their molecular volume, exhibit enhanced free radical scavenging
activity. In addition, molecules with an oxygen atom-bearing fragment (-OH, phenolic fragment,
Figure 4. Parent coumarin nucleus used in this study
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QSAR of Antioxidants
carbonyl fragment =O and others) as well as a tertiary carbon as substituents also lie in the higher
activity range. For the three-dimensional pharmacophore model that was developed using the BEST
method of conformer generation, the pharmacophoric features responsible for the explicit biological
activity of the molecules was identified. The best pharmacophore hypothesis, selected based on the
Correlation Coefficient and the cost functions of the developed models, indicate that three hydrogen
bond acceptor features and a hydrophobic feature lying at the specified distances from each other were
chiefly responsible for the optimum antioxidant activity profile of the molecules. The importance of
the hydrogen bond acceptor features was in good agreement with the descriptor-based QSAR model,
which reveals the significance of oxygen atom-bearing fragments to the overall activity profile of the
molecules. Furthermore, the contribution of the different molecular fragments to the overall activity
of the coumarin derivatives was identified based on the HQSAR analysis. The contribution map for
the HQSAR model revealed the importance of the parent coumarin nucleus for the optimal activity
of the molecules. The results appropriately go with those of the pharmacophore analysis, which indicates the importance of the fused benzene ring and the oxygen atom of the pyran ring for capturing
the hydrophobic feature and one of the hydrogen bond acceptor features, respectively. Consequently,
the diverse QSAR techniques applied in this study correlate well with each other. As a conclusion,
this work outlines the importance of the different molecular fragments or features at various positions
favoring the antioxidant activity profile as inferred from the three different models: descriptor based
QSAR, 3D pharmacophore and HQSAR model.
In 2012 (Martínez-Martínez, Razo-Hernández, Peraza-Campos, Villanueva-García, SumayaMartínez, Cano & Gómez-Sandoval,) performed synthesis and in vitro antioxidant activity evaluation
of 3-carboxycoumarin derivatives and QSAR study of their DPPH radical scavenging activity. In
this study, 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. The
two compounds 3-Acetyl-6-hydroxy-2H-1-benzopyran-2-one and ethyl 6-hydroxy-2-oxo-2H-1benzopyran-3-carboxylate presented the best radical-scavenging activity. A QSAR study was performed and correlated with the experimental DPPH scavenging data. The authors 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. The authors developed a Back Propagation Neural Network (BPNN) model 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. In genetic analysis, the average percent error obtained
was 3.77% while in back propagation neutral network, the average percent error was 7.18%. This
result indicates that the combination of the two methodologies optimize the creation of QSAR
models. The genetic analysis allows finding the most important descriptor for the development of
the antiradical activity and artificial neural network improves the 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 hydrophilic factor (Hy) descriptor could indicate that antiradical
activity would increase as we incorporate hydroxyl groups in the coumarin molecules. In conclusion, this study indicates the importance of the –OH hydrophilic group as crucial for antiradical
activity of coumarins. In other words, the models indicate that the antiradical activity increases as
we incorporate hydrophilic groups to the coumarin molecules.
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CONCLUSION
Computational-based rational drug design has become more common over the past decade. Most of the
approaches involved in this type of design focus on Quantitative Structure–Activity Relationship (QSAR)
studies that use various molecular descriptors for chemical information training. QSARs represent an
attempt to correlate physicochemical or structural descriptors of a set of compounds with their biological
(pharmacological, toxicological) activities.
In this chapter, we have summarized all effort done in building QSAR models of antioxidants. The
activity of the antioxidant compounds as well as their structural variety makes them a rich resource
for modeling lead compounds with targeted pharmacological properties. Different QSAR studies have
been reviewed related to flavonoids, hydroxyphenylureas, curcumin analogues, hydroxybenzalacetones,
phenolic derivatives bearing NO donor groups and coumarin derivatives. Therefore, the outcome of this
study could be used as a guide for further development of selective or the predictive ability of newly
designed antioxidative analogues, prior to synthesis.
In conclusion, different in silico methods were presented in this chapter especially QSAR in order
to account for the activity of different antioxidant compounds. In these QSAR studies, different statistical methods were used in order to build QSAR models using different types of descriptors. As it can
be seen from these studies, the authors tried to use the best validated QSAR models in order to design
new compounds with increased antioxidant activity. Therefore, it seems that designing new antioxidant
compounds using QSAR methodology requires novel descriptors such as the work done by Deeb and
Clare (Deeb & Clare, 2007) in which they have investigated the flavonoid-inhibitory activity of 54 analogues using the nodal angle descriptors (Clare 2000; Clare & Supuran, 2005) and flipstep regression
analysis. Also a combination of QSAR and docking or other methods will be the choice in designing
new antioxidant compounds. For example, the recent study (Mitra, Saha & Roy, 2013) in which the
authors performed predictive modeling of antioxidant coumarin derivatives using different approaches
that include: Descriptor based QSAR, 3 dimensional pharmacophore mapping and Hologram Quantitative Structure-Activity Relationship (HQSAR).
Many QSAR studies have been performed regarding the antioxidant activity of different series of
compounds. However, these QSAR studies are similar and sometimes repeated on the same class of
compounds with a minor improvement. It can be suggested that the use of innovative or novel descriptors
in the QSAR studies could be a new direction for this field of study. The suggested method will assure
the designing of new compounds with antioxidant activity.
REFERENCES
Allen, D. M. (1974). The relationship between variable selection and data augmentation and a method
for prediction. Technometric, 16(1), 125–127. doi:10.1080/00401706.1974.10489157
Antolovic, M., Prenzler, P. D., Patsalides, E., McDonald, S., & Robards, K. (2002). Methods for testing
antioxidant activity. Analyst (London), 127(1), 183–198. doi:10.1039/b009171p PMID:11827390
Benzie, I. F. (2003). Evolution of dietary antioxidants. Comparative Biochemistry and Physiology.
Part A, Molecular & Integrative Physiology, 136(1), 113–126. doi:10.1016/S1095-6433(02)00368-9
PMID:14527634
232
EBSCOhost - printed on 2/14/2023 7:16 AM via . All use subject to https://www.ebsco.com/terms-of-use
QSAR of Antioxidants
Chen, B., Zhu, Z., Chen, M., Dong, W., & Li, Z. (2014). Three-dimensional quantitative structure–activity relationship study on antioxidant capacity of curcumin analogues. Journal of Molecular Structure,
1061, 134–139. doi:10.1016/j.molstruc.2013.12.083
Clare, B. W. (1998). The frontier orbital phase angles: Novel QSAR descriptors for benzene derivatives, applied to phenylalkylamine hallucinogens. Journal of Medicinal Chemistry, 41(20), 3845–3856.
doi:10.1021/jm980144c PMID:9748359
Clare, B. W. (2000). The frontier orbital phase angles: A theoretical interpretation. Theochem, 507(1-3),
157–164. doi:10.1016/S0166-1280(99)00392-9
Clare, B. W. (2001a). Erratum to “The frontier orbital phase angles: A theoretical interpretation”. Theo-
chem, 535(1-3), 301–301. doi:10.1016/S0166-1280(00)00847-2
Clare, B. W., & Supuran, C. T. (2005). A physically interpretable quantum-theoretic QSAR for some
carbonic anhydrase inhibitors with diverse aromatic rings, obtained by a new QSAR procedure. Bio-
organic & Medicinal Chemistry, 13(6), 2197–2211. doi:10.1016/j.bmc.2004.12.055 PMID:15727872
Clare, B. W., & Supuran, C. T. (2005a). Predictive flip regression: A technique for QSAR of derivatives
of symmetric molecules. Journal of Chemical Information and Modeling, 45(5), 1385–1391. doi:10.1021/
ci050191v PMID:16180915
Deeb, O., & Clare, B. W. (2007). QSAR of aromatic substances: Protein tyrosine kinase inhibitory
activity of flavonoid analogues. Chemical Biology & Drug Design, 70(5), 437–449. doi:10.1111/j.1747-
0285.2007.00578.x PMID:17927721
Deeb, O., Shaik, B., & Agrawal, V. (2014). Exploring QSARs of the interaction of flavonoids with
GABA (A) receptor using MLR, ANN and SVM techniques. Journal of Enzyme Inhibition and Medicinal
Chemistry, 29(5), 670–676. doi:10.3109/14756366.2013.839557 PMID:24102524
Deeb, O., Youssef, K., & Hemmateenejad, B. (2008). QSAR of novel hydroxyphenylureas as antioxidant
agents. QSAR & Combinatorial Science, 27(4), 417–424. doi:10.1002/qsar.200730023
Devillers, J., & Balaban, A. T. (1999). Topological indices and related descriptors in QSAR and QSPR.
Amsterdam: Gordon Breach Scientific Publishers.
Downs, G. M. (2004). Molecular descriptors. In Computational medicinal chemistry for drug discovery
(pp. 515-538). Marcel Dekker.
Dragan, A., Dusanka, D., Drago, B., Vesna, R., Bono, L., & Nenad, T. (2007). Article. Current Medicinal
Chemistry, 14, 827–845. doi:10.2174/092986707780090954 PMID:17346166
Duprat, A. F., Huynh, T., & Dreyfus, G. (1998). Toward a principled methodology for neural network
design and performance evaluation in QSAR: Application to the prediction of log P. Journal of Chemical
Information and Computer Sciences, 38(4), 586–594. doi:10.1021/ci980042v PMID:9691473
Eriksson, L., Johansson, E., Kettaneh-Wold, N., & Wold, S. (2001). Multi- and megavariate data analysis.
principles and applications. Umea: Umetrics.
EBSCOhost - printed on 2/14/2023 7:16 AM via . All use subject to https://www.ebsco.com/terms-of-use
233
QSAR of Antioxidants
Free, S. M., & Wilson, J. W. (1964). A mathematical contribution to structure-activity studies. Journal
of Medicinal Chemistry, 7(4), 395–399. doi:10.1021/jm00334a001 PMID:14221113
Fujita, T. (1990). The extrathermodynamic approach to drug design. In Comprehensive medicinal
chemistry (Vol. 4). Pergamon.
Fujita, T., Iwasa, J., & Hansch, C. (1964a). A new substituent constant, π, derived from partition coefficients. Journal of the American Chemical Society, 86(23), 5175–5180. doi:10.1021/ja01077a028
Gunn, S. R. (1997). Support vector machines for classification and regression. University of Southampton.
Halvorsen, B. L., Carlsen, M. H., Phillips, K. M., Bohn, S. K., & Holte, K. (2006). Content of redoxactive compounds (i.e., antioxidants) in foods consumed in the United States. The American Journal of
Clinical Nutrition, 84, 95–135. PMID:16825686
Halvorsen, B. L., Holte, K., Myhrstad, M. C. W., Barikmo, I., & Hvattum, E. (2002). A systematic
screening of total antioxidants in dietary plants. The Journal of Nutrition, 132, 461–471. PMID:11880572
Hansch, C. (1969). A quantitative approach to biochemical structure-activity relationships. Accounts of
Chemical Research, 2(8), 232–239. doi:10.1021/ar50020a002
Hansch, C., & Fujita, T. (1964b). ρ−σ−π analysis. A method for the correlation of biological activity
and chemical structure. Journal of the American Chemical Society, 86(8), 1616–1626. doi:10.1021/
ja01062a035
Hansch, C., Maloney, P., Fujita, T., & Muir, R. M. (1962). Correlation of biological activity of phenoxyacetic acids with hammett substituent constants and partition coefficients. Nature, 194(4824), 178–180.
doi:10.1038/194178b0
Hemmateenejad, B., Mehdipour, A., Deeb, O., Sanchooli, M., & Miri, R. (2011). Toward an optimal
approach for variable selection in counter-propagation neural networks: Modeling protein-tyrosine kinase
inhibitory of flavanoids using substituent electronic descriptors. Molecular Informatics., 30(11-12),
939–949. doi:10.1002/minf.201100081
Jayaprakasha, G. K., Jena, B. S., Negi, P. S., & Sakariah, K. K. (2002). Evaluation of antioxidant activities and antimutagenicity of turmeric oil: A byproduct from curcumin production. Zeitschrift fur
Naturforschung., 57c, 828–835. PMID:12440720
Jolliffe, I. T. (1986). Principal component analysis. New York: Springer-Verlag. doi:10.1007/978-14757-1904-8
Karelson, M. (2000). Molecular descriptors in QSAR/QSPR. New York: Wiley-InterScience.
Katritzky, A. R., Lobanov, V. S., & Karelson, M. (1994). CODESSA, reference manual. University of
Florida. Retrieved from http://www.semichem.com/codessa
Knight, J. (1998). Free radicals: Their history and current status in aging and disease. Annals of Clinical
and Laboratory Science, 28(6), 331–346. PMID:9846200
234
EBSCOhost - printed on 2/14/2023 7:16 AM via . All use subject to https://www.ebsco.com/terms-of-use
QSAR of Antioxidants
Martínez-Martínez, J., Razo-Hernández, R., Peraza-Campos, A., Villanueva-García, M., Sumaya-Martínez,
M., Cano, D., & Gómez-Sandoval, Z. (2012). Synthesis and in vitro antioxidant activity evaluation of
3-carboxycoumarin derivatives and QSAR study of their DPPH radical scavenging activity. Molecules
(Basel, Switzerland), 17(12), 14882–14898. doi:10.3390/molecules171214882 PMID:23519260
Matill, H. A. (1947). Antioxidants. Annual Review of Biochemistry, 16(1), 177–192. doi:10.1146/annurev.bi.16.070147.001141 PMID:20259061
Milardovic, S., Ivekovic, D., & Grabaric, B. S. (2006). A novel amperometric method for antioxidant
activity determination using DPPH free radical. Bioelectrochemistry (Amsterdam, Netherlands), 68(2),
175–180. doi:10.1016/j.bioelechem.2005.06.005 PMID:16139574
Mitra, I., Saha, A., & Roy, K. (2011). Chemometric QSAR modeling and in silico design of antioxidant
no donor phenols. Scientia Pharmaceutica, 79(1), 31–57. doi:10.3797/scipharm.1011-02 PMID:21617771
Mitra, I., Saha, A., & Roy, K. (2013). Predictive modeling of antioxidant coumarin derivatives using
multiple approaches: descriptor-based QSAR, 3D-pharmacophore mapping, and HQSAR. Scientia
Pharmaceutica, 81(1), 57–80. doi:10.3797/scipharm.1208-01 PMID:23641329
Montgomery, D. C., & Peck, E. A. (1992). Introduction to linear regression analysis. New York: Wiley.
Novi, M., Nikolovska-Coleska, Z., & Solmajer, T. (1997). Quantitative structure-activity relationship
of flavonoid p56 protein tyrosine kinase inhibitors. A neural network approach. Journal of Chemical
Information and Computer Sciences, 37(6), 990–998. doi:10.1021/ci970222p
Pisoschi, A. M., Cheregi, M. C., & Danet, A. F. (2009). Total antioxidant capacity of some commercial
fruit juices: Electrochemical and spectrophotometrical approaches. Molecules (Basel, Switzerland),
14(1), 480–493. doi:10.3390/molecules14010480 PMID:19158657
Rasulev, B., Abdullaev, N., Syrov, V., & Leszczynski, J. (2005). A quantitative structure-activity relationship (QSAR) study of the antioxidant activity of flavonoids. QSAR & Combinatorial Science, 24(9),
1056–1065. doi:10.1002/qsar.200430013
Sarkar, A., Middya, T. R., & Jana, A. D. (2012). A QSAR study of radical scavenging antioxidant activity of a series of flavonoids using DFT based quantum chemical descriptors – The importance of group
frontier electron density. Journal of Molecular Modeling, 18(6), 2621–2631. doi:10.1007/s00894-0111274-2 PMID:22080306
Sies, H. (1997). Oxidative stress: Oxidants and antioxidants. Experimental Physiology, 82(2), 291–295.
doi:10.1113/expphysiol.1997.sp004024 PMID:9129943
Silipo, C., & Vittoria, A. (1990). Three-dimensional structure of drugs. Medicinal Chemistry (Shariqah,
United Arab Emirates), 4, 154–204.
Tetko, I. V., Alessandro, E., Villa, P., & Livingstone, D. J. (1996). Neural network studies. 2. Variable
selection. Journal of Chemical Information and Computer Sciences, 36(4), 794–803. doi:10.1021/
ci950204c PMID:8768768
EBSCOhost - printed on 2/14/2023 7:16 AM via . All use subject to https://www.ebsco.com/terms-of-use
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