Topological sub-structural molecular design approach: Radical scavenging activity

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Original article

Topological sub-structural molecular design approach: Radical scavenging activity

Alfonso Pérez-Garrido a,b,*, Aliuska Morales Helguera c,d,e, Juana M. Morillas Ruiz a,b,Pilar Zafrilla Rentero b

aCátedra de Ingeniería y Toxicología Ambiental, Universidad Católica San Antonio, Guadalupe, Murcia, C.P. 30107, SpainbDepartamento de Tecnología de la Alimentación y Nutrición, Universidad Católica San Antonio, Guadalupe, Murcia, C.P. 30107, SpaincCIQUP/Departamento de Química e Bioquímica, Faculdade de Ciências, 4169-007, Universidade do Porto, Porto, PortugaldDepartamento de Química, Universidad Central Marta Abreu de Las Villas, Santa Clara, 54830 Villa Clara, CubaeCBQ, Universidad Central “Marta Abreu” de Las Villas, Santa Clara, 54830 Villa Clara, Cuba

a r t i c l e i n f o

Article history:Received 7 September 2011Received in revised form14 December 2011Accepted 20 December 2011Available online 4 January 2012

Keywords:QSARSpectral momentsTOPS-MODERadical scavenging activityFlavonoidsAntioxidant activity

a b s t r a c t

In the last decades phenolic compounds have gained enormous interest because of their beneficial healtheffects such as anti-inflammatory, anticancer, or antiviral activities. The pharmacological effects ofphenolic compounds are mainly due to their antioxidant activity and their inhibition of certain enzymes.This antoxidant activity is related to the structure and has been extensively reported throught SAR orQSAR models. These studies confirmed that the number and position of hydroxyl groups, the relatedglycosylation and other substitutions in the phenolic ring largely determined radical scavenging activity.Most of these models are based on certain families of chemicals (flavonoids, cinnamic acids, etc.) andthe model by itself is not useful for other substances of a different family. In this study we developeda QSAR model for a heterogeneous group of substances with TOPS-MODE descriptors for an interpre-tation of the antioxidant activity of these compounds in the form of bond contributions. The modeldeveloped, able to describe more than 90% of the variance in the experimental activity, also has a goodpredictive ability and stability. The information extracted from the QSAR model revealed that the majordriving forces for radical scavenging activity are hydrogen bond donation and polarity. With this work wehave managed to unify the different families of antioxidants in a single model with sufficient capacity tomake predictions of radical scavenging activity for unknown substances.

� 2011 Elsevier Masson SAS. All rights reserved.

1. Introduction

Phenolic compounds have gained interest due to theirnumerous beneficial health effects that have been proven over theyears. These beneficial effects are anti-inflammatory, anticancerand antiviral activities [1e3], derived from the pharmacologicaleffects which occur due to antioxidant activity [4,5] and theirinhibition of certain enzymes [6,7]. SAR and QSAR models havebecome a useful tool not only for predicting activities of unknownsubstances, but also to better understand the mechanism of actionof these substances, especially in the case of antioxidants [8e13]. Aseries of relationships between the structure and antioxidantactivity for each family of substances are known to date (Fig. 1).

� Flavonoids increase their antioxidant activity following thewell-known three Bors’ criteria [14].

e The ortho-dihydroxy structure in the B ring, which confershigh stability to the flavonoid phenoxyl radicals viahydrogen bonding or by expanded electron delocalization.

e The C2eC3 double bond (in conjugation with the 4-oxogroup), which determines the coplanarity of the heterocy-clic ring and participates in radical stabilization via electrondelocalization over all three ring systems.

e The presence of both 3-OH and 5-OH groups for themaximal radical scavenging capacity and the strongestradical absorption.

� In stilbenes, a greater number of hydroxyl produces a higherantioxidant power. The ortho-dihydroxy groups and the40para-hydroxyl group played an important role in scavenging[15] and the glycosylation of the hydroxyl groups markedlyreduced the activity [16].

� In curcuminoids the para-hydroxyl groupmay play amajor rolein scavenging free radicals [17]. The enol form (b-diketone)exhibited the strongest antioxidant activity and the methoxygroups significantly improved the radical scavenging activity[15,17].

* Corresponding author. Cátedra de Ingeniería y Toxicología Ambiental, Uni-versidad Católica San Antonio, Guadalupe, Murcia, C.P. 30107, Spain. Tel.: þ34 968278 755.

E-mail address: [email protected] (A. Pérez-Garrido).

Contents lists available at SciVerse ScienceDirect

European Journal of Medicinal Chemistry

journal homepage: http: / /www.elsevier .com/locate/ejmech

0223-5234/$ e see front matter � 2011 Elsevier Masson SAS. All rights reserved.doi:10.1016/j.ejmech.2011.12.030

European Journal of Medicinal Chemistry 49 (2012) 86e94


� In coumarins ortho-hydroxyl groups improved the activity andsubstitution of this group by sugar and methoxy groupconsiderably reduced the radical scavenging level [18].

� In quinones, as for the coumarins, the presence of ortho-hydroxyl groups increased the antioxidant activity and glyco-silacin of these markedly diminished their radical scavengingactivity [15,19].

Update, the vast majority of published QSAR models have beendeveloped for congeneric series of chemicals [13]. Nevertheless, thenumber of QSARs for different individual chemical classes isinsufficient to be considered useful for screening antioxidant drugcandidates in large databases. For this reason there is interest inmodelling the antoxidant activity of heterogeneous collections ofcompounds. The objectives of this study are (i) to perform a QSARmodel using different families of antioxidants as training set andTOPS-MODE approach as molecular descriptors and (ii) to interpretthe antioxidant activity of these compounds in term of bondcontributions. These TOPS-MODE descriptors are easy to calculateand have been used successfully with heterogeneous series ofcompounds [20e24].

2. Materials and methods

2.1. Dataset

Theoverall data set of 97 substances comprised a large number ofclasses of antioxidant compounds: flavanols, hydroxybenzoic acids,

hydroxycinnamic acids, chalcones, flavones, flavonols, stilbenes,curcuminoids, coumarins, hydroxyanthraquinones, flavanones,lignans, naphthoquinones, isoflavones and tannins (Table 1). This setof guest molecules was extracted from the work of Cai et al. [15],and the experimental activity to be predicted are the radical scav-enging activitywith ABTS�þ [2,2V-azinobis(3-ethylbenzothiazoline-6-sulfonic acid)] expressed in terms of Trolox equivalent antioxidantcapacity (TEAC/mol L�1) [25,26]. In order to assess the modelpredictive abilitywe developed themodels usingonly the 80% of thechemical available as training set and the other 20% for the externalvalidation. The distribution of the number of compounds by chem-ical class in the training and the test set is given in Table 2 and theselection was made randomly.

2.2. The TOPS-MODE descriptors

Here, we have resorted to the graphed-based moleculardescriptors, spectral moments, incorporated in the TOPologicalSub-Structural Molecular Design (TOPS-MODE) approach andimplemented by MODESLAB software [27], available at the Internetsite http://www.modeslab.com. The spectral moments are definedas the traces of the bond adjacencymatrix [28e30]. That is, the sumof the main diagonal elements of different powers of such matrix.The bond adjacency matrix is a squared symmetric matrix whoseentries are ones or zeros if the corresponding bonds are adjacent ornot, respectively. The order of this matrix (m) is the number ofbonds in the molecular graph, being two bonds adjacent if they areincident to a common atom. Furthermore, weights are introduced

Fig. 1. General structure of different antioxidants.

A. Pérez-Garrido et al. / European Journal of Medicinal Chemistry 49 (2012) 86e94 87


in the diagonal entries of this matrix to mirror fundamentalphysicochemical properties that might relate to the target endpointbeing modelled. Here, several bond weights were used forcomputing the spectral moments, namely the standard bonddistance (Std), standard bond dipole moments (Dip, Dip2), hydro-phobicity (Hyd), polar surface area (Pols), polarizability (Pol), molarrefractivity (Mol), van derWaals radii (vdW), and Gasteiger-Marsillicharges (Gas), atomic masses (Ato), solute excess molar refraction(Ab � R2), solute dipolarity/polarizability (Ab � pH

2 ), effective

hydrogen-bond basicity (Ab � PbO2 , Ab � P

bH2 ) and solute gas-hexadecane partition coefficient (Ab � logL16) were used forcomputing the spectral moments of the bond matrix.

We have calculated the first 15 spectral moments (m1 � m15) foreach bond weight and the number of bonds in the molecules (m0)withhydrogens. Also,wemultipliedm0 andm1 for thefirst 15 spectralmoments obtaining 30 new variables. Notice that in this way suchvariables might offset the linear approximation assumption of themodel. As described previously [31], the atomic contributions werethen transformed into bond contributions as follows:

wði; jÞ ¼ wi

diþwj

dj(1)

Table 1Names, observed (TEACO) and predicted (TEACP) activity, and leverage (h) values forthe compounds used in this study.

Compounds Name TEACOa TEACpb Partition h

1 Procyanidin B2 digallate 9.180 9.194 Training e

2 Procyanidin C1 8.290 8.333 Training e

3 Corilagin 7.760 7.595 Training e

4 (�)-Epigallocatechin gallate 5.950 6.076 Training e

5 (�)-Epicatechin gallate 5.270 4.586 Training e

6 Quercetin 4.420 3.152 Training e

7 Gallic acid 3.520 2.776 Training e

8 (�)-Epicatechin 3.060 2.292 Training e

9 Morin 2.680 3.167 Training e

10 Baicalein 2.560 1.305 Training e

11 Piceatannol 2.530 2.805 Training e

12 Butein 2.420 2.655 Training e

13 Quercetin 3 glucoside 2.390 2.679 Training e

14 Esculetin 2.380 0.774 Training e

15 Curcumin 2.240 1.748 Training e

16 Quercitrin 2.180 1.827 Training e

17 Luteolin 2.180 2.137 Training e

18 Resveratrol 2.140 1.946 Training e

19 Rutin 2.020 1.706 Training e

20 para-Coumaric acid 1.960 1.075 Training e

21 Sappanchalcone 1.930 1.892 Training e

22 Purpurin 1.930 0.908 Training e

23 Ferulic acid 1.920 1.334 Training e

24 Phloretin 1.790 1.720 Training e

25 Piceatannol-30-glucoside 1.620 1.642 Training e

26 Pseudopurpurin 1.620 1.810 Training e

27 Chlorogenic acid 1.560 1.407 Training e

28 Baicalin 1.550 0.410 Training e

29 Isoferulic acid 1.530 1.335 Training e

30 Syringic acid 1.390 1.437 Training e

31 2, 4-Hydroxybenzoic acid 1.220 1.727 Training e

32 Bisdemethoxycurcumin 1.180 1.748 Training e

33 Galangina 1.120 1.306 Training e

34 Alizarin 1.070 0.226 Training e

35 ortho-Coumaric acid 0.930 1.079 Training e

36 meta-Coumaric acid 0.820 1.077 Training e

37 Flavonol 0.707 0.178 Training e

38 Resveratrol-40-glucoside 0.558 0.588 Training e

39 Hesperetin 0.403 0.863 Training e

40 Secoisolariciresinol 0.308 �0.143 Training e

41 Matairesinol 0.253 0.737 Training e

42 Naringenin 0.217 0.682 Training e

43 Vitexin 0.216 0.814 Training e

44 Esculetin-6-glucoside 0.164 0.893 Training e

45 Astragalin 0.138 1.470 Training e

46 Shikonin 0.124 0.289 Training e

47 Genistein 0.123 1.301 Training e

48 Alizarin-2-glucoside 0.105 �0.335 Training e

49 Juglone 0.105 �0.059 Training e

50 Hesperidin 0.104 0.243 Training e

51 Arctigenin 0.104 0.433 Training e

52 Daidzein 0.101 0.647 Training e

53 Vanillic acid 0.092 1.166 Training e

54 Apigenin 0.086 1.306 Training e

55 Apigetrin 0.083 0.406 Training e

56 Genistin 0.077 0.401 Training e

57 Aloe-emodin 0.077 0.134 Training e

58 Rhein 0.076 0.929 Training e

59 1,5-Dihydroxyanthraquinone 0.076 0.237 Training e

60 Ruberythic acid 0.073 �0.005 Training e

61 Daidzin 0.072 �0.455 Training e

62 2, 6-Dihydroxyanthraquinone 0.072 0.218 Training e

63 Chrysophanol 0.069 �0.002 Training e

64 Physcion 0.068 0.019 Training e

65 Chrysazine 0.068 0.237 Training e

66 para-Hydroxybenzoic acid 0.028 0.849 Training e

67 meta-Hydroxybenzoic acid 0.025 0.851 Training e

68 Anthraquinone 0.009 �0.567 Training e

69 trans-Cinnamic acid 0.007 0.399 Training e

70 Benzoic acid 0.005 0.161 Training e

71 Isoflavone 0.005 �0.086 Training e

72 5-Metoxifuranocoumarin 0.003 0.046 Training e

73 Flavone 0.003 �0.100 Training e

74 trans-Stilbene 0.002 0.461 Training e

Table 1 (continued)

Compounds Name TEACOa TEACpb Partition h

75 Coumarin 0.001 �0.252 Training e

76 trans-Chalcone 0.001 0.133 Training e

77 Flavanone 0.000 �0.855 Training e

78 Procyanidin B1 6.140 5.519 Test 0.30779 (�)-Epigallocatechin 3.710 3.513 Test 0.19180 Demethoxycurcumin 1.630 1.771 Test 0.16181 Kaempferol 1.590 2.137 Test 0.05182 Quercetin 3 glucoside 7 rham 1.560 1.549 Test 0.32183 Cinaroside 1.470 1.429 Test 0.06984 Carthamin 1.430 1.943 Test 0.12985 Resveratrol-3-glucoside 1.350 0.582 Test 0.08586 Caffeic acid 1.310 1.933 Test 0.06287 Protocatechuic acid 1.150 1.720 Test 0.07488 Quinizarin 0.548 0.237 Test 0.03789 Scopoletin 0.383 0.364 Test 0.02890 Magnolol 0.209 0.394 Test 0.10291 Acetylskikonin 0.107 �0.410 Test 0.32692 Naringin 0.098 0.050 Test 0.27393 Glycitein 0.097 0.723 Test 0.05694 Emodin 0.095 0.632 Test 0.05095 Chrysin 0.081 0.658 Test 0.04496 ortho-Hydroxybenzoic acid 0.037 0.854 Test 0.071

a Experimental values of TEAC.b TEAC values predicted by Eq. (5).

Table 2Number of substances extracted from each family.

Training Test

Flavanols 3 1Hydroxybenzoic acids 7 2Hydroxycinnamic acids 7 1Chalcones 4 1Flavones 7 2Flavonols 8 2Stilbenes and curcuminoids 7 2Coumarins 4 1Anthraquinones and naphthoquinones 15 3Flavanones 4 1Lignans 3 1Isoflavones 5 1Taninns 3 1TOTAL 77 19

A. Pérez-Garrido et al. / European Journal of Medicinal Chemistry 49 (2012) 86e9488


where wi and di are the atomic weight and vertex degree of theatom i. Calculation of the TOPS-MODE descriptors was carried outfrom the SMILES (Simplified Molecular Input Line Entry System)notation available for each compound [32]. To develop thestructureeproperty relationships, the following 7-step paths wereadopted:

1. Split the dataset into training set and test set.2. Draw the molecular graphs for each molecule included in the

training set and test set.3. Compute the spectral moment using an appropriate set of

weights.4. Find an adequate QSAR model from the training set by

a regression-based approach.5. Subject the derived QSAR model to rigorous internal and

external validation, thereby assessing the performance of themodel in regard to their applicability and predictability.

6. Compute the contribution of the different substructures todetermine their role on to the antioxidant activity of thestudied molecules.

2.3. Variable selection

There is a vast amount and wide range of molecular descriptorswith which one can model the activity of interest. This makes thesearch for gathering themost suitable subset quite complicated andtime consuming because of the many possible combinations,especially if one tries to define an accurate, robust, and (above all)interpretable model. For this reason, we applied the GeneticAlgorithm (GA) procedure [33] for selecting the variables, asimplemented in the Mobydigs software (v1.0) [34]. The particularGA simulation applied here resorted to the generation of 100regression models, ordered according to their increased internalpredictive performance (verified by leave one out cross-validation).Fist of all, models with one to two variables were developed by thevariable subset selection procedure in order to explore all lowcombinations. The number of descriptors was subsequentlyincreased one by one, and newmodels formed. The GAwas stoppedwhen further increments in the size of the model did not increaseinternal predictivity in any significant degree. Furthermore, thefollowing GA simulation conditions were used: the maximumnumber of variables in a model was 10, the number of best retainedmodels for each size was 5, the trade off between crossovers andmutation parameter (T) was from 0.3 to 0.7, and selection bias (B%)was from 30 to 90.

2.4. Model validation

Two kinds of diagnostic statistical tools were used for evaluatingthe performance of our regressionmodel: the so-called goodness offit and goodness of the prediction. In the first case, attention isgiven to the fitting properties of the model, whereas in the secondcase attention is paid to the predictive power of the model (i.e., themodel adequacy for describing new compounds). In this work,random selectionwas used to split the original dataset of chemicalsinto training and test sets. On so doing, 78 out of 97 compoundswere selected as the training set and the rest (19) as the externaltest set.

Goodness of fit of the models was assessed by examining thedetermination coefficient (R2), the standard deviation (s), theFisher’s ratio (F) and the ratio between the number of cases and thenumber of adjustable parameters in the model (known as ther statistics; notice that r should be �4) [35].Other importantstatistics, namely the Kubinyi function (FIT) [36,37] and Akaike’s

information criteria (AIC) [38,39] were taken into account, as theygive enough criteria for comparing models with different param-eters, numbers of variables and numbers of chemicals.

As to the robustness and predictivity of the models, these wereevaluated by means of cross-validation, basically leave one out(CV-LOO) and bootstrapping testing techniques, by looking to theoutcome statistics of both techniques (i.e.: Q2

LOO and Q2boot) as well

as to the Q2EXT values obtained with the test set substances that fall

within the applicability domain of the model. Further, the stabilityunder heavy perturbations in the training set was checked byexamining the outcome statistics of a response randomizationprocedure (Y scrambling) for the training and test sets (a(R2) anda(Q2) values). All these calculations were carried out with softwareMobydigs (v1.0) [34].

To sum up, good quality of the models is indicated by high F, FITand r values, by low s and AIC values, as well as by values closed toone for R2, Q2

LOO, Q2boot and Q2

EXT (save for a(R2) and a(Q2) values,which check random correlations). The spectral moments areinherently collinear. From the point of view of QSPR modelling, themain drawback of collinearity is that it increases the standarderrors associated with the individual regression coefficients,thereby decreasing their value for purposes of interpretability. Toovercome this problem, we have employed here the Randi�c’smethod of orthogonalisation [40e44]. Firstly, one has to select theappropriate order of orthogonalisation, which, in this case, is theorder of significance of the variables in the model. The first variable(v1) is taken as the first orthogonal-descriptor and the second one isorthogonalised with respect to it by taking the residual of itscorrelation with v1. The process is repeated until all variables arecompletely orthogonalised, after which they are further stand-arised. Orthogonal standarised variables are then used to obtaina new model. For extracting of the information contained in theorthogonalised descriptors we followed the procedure reported byEstrada et al. [20].

2.5. Fragment contribution to antioxidant activity

The identification of bond contributions, implemented inMODESLAB software, consists in transforming a QSAR model intoa bond additive scheme. Then, by summing up bond contributions,one can detect the fragments on a given molecule that contributepositively or negatively to the antioxidant activity and forward aninterpretation of their effects in terms of physicochemical proper-ties. Bond contributions are derived from the local spectralmoments. They are defined as the diagonal entries of the differentpowers of the weighted bond matrix (B):

mTk ðiÞ ¼ biiðTÞk (2)

where mTk ðiÞ is the k-th local spectral moment of the bond i, bii(T) arethe diagonal entries of the weighted B matrix and T is the type ofbond weight. For a given molecule, we can substitute the values ofthe local spectral moments computed by Eq. (2) into Eq. (3) andthus gather the total contribution to the complexation of itsdifferent bonds.

P ¼ b0 þX

k

ak$mTk (3)

Since the activity modelled is expressed as TEAC, positive bondcontributions increase the TEAC value and increase the antioxidantactivity and viceversa. The structural information highlighted bythe bond contributions may allow, along with other theoretical andexperimental data, for a better understanding of themechanisms ofantioxidant activity of the involved chemicals.



2.6. Applicability domain of the models

Given that the real utility of a QSAR model relies on its ability toaccurately predict the modelled activity for new chemicals, carefulassessment of the model’s true predictive power is a must. Thisincludes the model validation but also the definition of the appli-cability domain of the model in the space of molecular descriptorsused for deriving themodel. There are severalmethods for assessingthe applicability domain ofQSAR/QSPRmodels [45,46], but themostcommonone encompasses determining the leverage values for eachcompound [47]. AWilliams plot, i.e. the plot of standarized residualsversus leverage values (h), can then be used for an immediate and

simple graphical detection of both the response outliers and struc-turally influential chemicals in the model. In this plot, the applica-bility domain is established inside a squared area within �xstandarddeviations and a leverage thresholdh* (h* is generallyfixedat 3 k/n, where n is the number of training compounds and k thenumber of model parameters plus one, whereas x ¼ 2 or 3), lyingoutside this are (vertical lines) the outliers and (horizontal lines)the influential chemicals. For future predictions, only predictedcomplex stability constant data for chemicals belonging to thechemical domain of the training set should be proposed and used[48]. So, calculations of Q Q2

EXT were performed only for thosesubstances that had a leverage value below the threshold h*.

3. Results and discussion

3.1. QSAR model

According to the strategy outlined before, we began by seekingthe best linear model relating the antioxidant activity with theTOPS-MODE descriptors for the training set. The resulting best-fitmodel (a ten-variable equation) is given below along with theMultilinear regression (MLR) statistics. As seen, this model is goodboth statistical significance and goodness of fit.

As can be observed, the model is good both in terms of theirstatistical significance and predictive ability backed up by the smallp and large F values. The high value of the determination coefficientR2 points out the goodness of model fit. The robustness and

predictive ability of the model is indicated by the high Q2LOO, Q

2boot

and low a(R2) and a(Q2) values as well as the suitable values of theparameter derived from the prediction set Q2

EXT. Finally, the highvalue of r show that the model are not over-fitted by an excessof parameters; this parameter is expected to be >4 for linearmodels [35].

Another aspect deserving special attention is the degree ofcollinearity of the variables of the model. Following Randi�c’stechnique, we determined orthogonal complements for all vari-ables of the above non-orthogonalised model and the followingQSAR model was obtained:

where the symbol UiX means the orthogonal complement of vari-able X, the superscript referring to followed order in the ortho-gonalisation process. By comparing Eq. (4) with Eq. (5), it can beseen that there are no changes in the sign of the regression coef-ficients. Nevertheless, the relative contributions of the variables inthe orthogonal-descriptor model were different from those in thenon-orthogonalised model. For purposes of QSAR interpretability,we shall use the orthogonal-descriptor model defined in Eq. (5).

The variables weighted with effective hydrogen-bond basicity,polar surface area and dipole moment explained, 31.9%, 20% and17.9% of the variance respectively, for this specific training set ofchemicals (Table 3). To a lesser extent, the variables weighted withatomic weigth and solute excess molar refraction accounted for19.5% and 1.4% of the variance, respectively. This indicates that theH-atom donation (effective hydrogen-bond basicity) and polarity(dipole moment and polar surface area) could be the key mecha-nisms of radical scavenging activity. These mechanisms havealready been related previously by other authors [49e53]. Thereare several QSARmodels that correlate the antioxidant activity witha diverse set of molecular descriptors, which include topological,quantic, heat of reaction [8,13,15]. Among these studies, only threefoucous their efforts on predict the equivalent Trolox antioxidantcapacity [54e56], however they are limitated to small number of

chemicals belonging only to the flavonoid derivatives. Therefore, inthis work we achieve to extend the antioxidant QSAR study toa more diverse dataset (Table 2) and get similar goodness of fit andpredictivity to the before congeneric models.

TEAC ¼ 1:15$10�11 � 1:28$10�12$m1mDip15 þ 2:21$10�2 � 4:42$10�3$mAto1 � 2:54$10�8 � 5:19$10�9$m1m

Ab�P

bH2

15

þ1:33$10�7 � 2:76$10�8$m1mAb�

PbH2

14 þ 1:84$10�15 � 3:85$10�16$m1mPols9 � 4:82$10�4 � 1:04$10�4$m1m

Ab�P

bH2

8

þ1:42$10�3 � 3:08$10�4$m1mAb�

PbH2

7 � 6:87$10�6 � 1:68$10�6$m0mDip6 � 3:20$10�2 � 7:94$10�3$m

Ab�P

bH2

3

þ1:34$10�7 � 4:28$10�8$m0mAb�R2

9 � 1:81

n ¼ 77;R2 ¼ 0:907;Q2LOO ¼ 0:882; s ¼ 0:629; F ¼ 64:36;AIC ¼ 0:528; FIT ¼ 3:637; p < 0:001

r ¼ 7:7;Q2boot ¼ 0:821; a

�r2� ¼ 0:08; a

�Q2� ¼ �0:48;Q2

EXT ¼ 0:893:

(4)

TEAC¼ 7:1$10�3�6:07$10�4$U2mAto1 þ1:76$10�15�1:48$10�16$U5m1mPols9 þ1:18$10�8�1:08$10�9$U4m1m

Ab�P

bH2

14

þ1:54$10�12�1:56$10�13$U1m1mDip15 þ1:88$10�3�2:95$10�4$U7m1m

Ab�P

bH2

7 �9:29$10�11�1:62$10�11$U3m1mAb�

PbH2

15

�4:16$10�6�7:60$10�7$U8m0mDip6 �3:15$10�5�6:80$10�6$U6m1m

Ab�P

bH2

8 �2:74$10�2�7:81$10�3$U9mAb�

PbH2

3

þ1:34$10�7�4:28$10�8$U10m0mAb�R2

9 þ0:99

(5)



3.2. Fragments contribution identification

One advantage of the present approach for QSAR studies is thatit can provide information explaining how structural features ofmolecules can account for the activities. It is then possible to detectfragments that contribute positively or negatively to a particulartarget endpoint and their effects can be interpreted in terms ofphysicochemical properties [57]. Specifically in our case, thecontributions to radical scavenging activity for each of the selectedfragments (see Fig. 2) were extracted from the final orthogonal-descriptor model; these are shown in Table 4. A careful look atthese values allow us to find functional groups that either block theantioxidant activity, or enhanced it. Further, it might lead us todesign molecular structures that have a better profile for thephenomenon or to a rapid selection of the most favourablesubstance among a long list of substances.

One of the important features of the antioxidant activity of thesesubstances lies in the presence of hydroxyl groups on the benzene

Fig. 2. Selected molecular fragments (substructures with a solid line marked) for which their contributions to the radical scavenging activity were calculated.

Table 3Step-by-step analysis of the forward stepwise regression process of Eq. (5).

Variable included R2 DR2

U2mAto10.195 0.195

U5m1mPols9

0.395 0.200

U4m1mAb�

PbH2

140.563 0.168

U1m1mDip15

0.700 0.137

U7m1mAb�

PbH2

70.757 0.057

U3m1mAb�

PbH2

150.803 0.046

U8m0mDip6

0.846 0.042

U6m1mAb�

PbH2

80.876 0.030

U9mAb�

PbH2

30.893 0.017

U10m0mAb�R2

90.907 0.014



ring [15]. This characteristic is evident to the positive contributionto the radical scavenging activity observed for the fragment F1. Also,if we compare the values of contribution of the fragments F1, F8 andF10 can be noted that the number and proximity of these hydroxylgroups further enhances this activity. It is known that the proximityof hydroxyl groups favours the formation of phenoxyl radicals thatmay be stabilized by the mesomeric equilibrium to ortho-semi-quinone structures [58]. The negative contributions of F4, F5, F6 andF7 indicate that if these hydroxyl groups are glycosylated signifi-cantly reduce the antioxidant activity of these substances possiblybecause this reduces the number of free hydroxyl groups, destroysthe ortho-dihydroxil structure or the binding of sugar could hinderaccess of the substance to the radical centre of ABTSþ [15]. If wenow compare the values of F1 and F2 fragments we see that themethylation of these hydroxyl groups markedly decreases theantioxidant power, though not completely nullifies. For methoxygroups have shown that their presence in the molecule reduces theactivity for some flavonoids [59], but is increased for phenolic acidsand curcuminoids [60e62].

Other groups present as the galloyl moiety (F3) could consid-erably strengthen the activity because of its catecol structure withortho-dihydroxyl groups. The hydroxyl groups on the galloylmoieties contribute to antioxidant activity, making the compoundscapable not only of donating more hydrogen atoms but also ofproviding more chelating sites for scavenging catalytic cations[63e65].

Although to a lesser extent, the presence of acid groups alsoincreases the activity (F11). It is known that benzoic acid does notpossess antioxidant activity [15], but the presence of acid moiety inquinones contributes positively. As might be expected the presenceof hydroxyl groups in ortho position to the acid group favours theantioxidant activity (F12 and F13) [61,62].

Regarding flavonoids, there were no significant differencesbetween the contributions of hydroxyl groups in different positionsin the derived model (F8 and F9). For this family of compounds if wecompare the fragments contributions of F14 with F15 and F22 withF23 we emphasize that the presence of the 2,3 double bondconjugate with the 4-oxo function strengthen the activity ofthe flavonols due a stabilization of the aryloxyl radicals in A- andB- rings [8,61,66,67].

This same situation we see in the chalcones throught the frag-ments F16 and F17. In this case the contribution to the stabilization ofthe radical is lower of flavonols possibly due to lack of heterocyclicring (F14 and F16). The open structure of chalconeswould be difficultto stabilize the radical so they would have lower antioxidantactivity.

Another aspect derived of flavonoids is the presence of both3-OH and 5-OH groups, which increases the radical scavengingcapacity due to the strongest radical absortion [8]. This feature canbe seen by comparing the values of contribution of the fragmentsF20 and F22.

In curcuminoids it is known that the enol form (b-diketone)makes positive contribution to the antioxidant activity (F18) due tothe strong H-bond formed between the hydroxyl and ceto groups inthe enol form [17]. Besides para-hydroxyl groups (F19) in thesefamily are essential for the radical scavenging activity [15].

3.3. Applicability domain

It would be very interesting to have a predictive model for thevast majority of chemicals, particularly for thosewho have not beentested and, therefore, with unknown TEAC values. Since this isusually not possible, one should define the applicability domain ofthe QSAR model, that is, the range within which the model bearsa new compound.

Nevertheless, all evaluations pertaining to the external set wereperformed by taking into account the applicability domain of ourQSAR model. So, if a chemical belonging to the test set hada leverage value greater than h* (¼0.428), we consider that thismeans that the prediction is the result of substantial extrapolationand therefore may not be reliable [46]. All of the compounds of thetest set are within the applicability domain (Table 1) covered by�3times the standard residual (s) and the leverage threshold h*(¼0.428).

4. Conclusions

We developed a QSAR model to predict the antioxidant activityto a heterogeneous group of substances. Besides a good correlation,the model has good predictive ability and high stability. In addition,the descriptors used in the model are of great interest in molecularmodelling because they don’t require high computational resourcesto obtain them.

The information extracted from the QSAR model revealed thatthe major driving forces for radical scavenging activity arehydrogen bond donation (effective hydrogen-bond basicity) andpolarity (dipole moment and polar surface area). The forcesinvolved in the radical scavenging activity as well as bond contri-butions obtained correspond to the relations found in the literature.With this work we have managed to unify the different families ofantioxidants in a single model with sufficient capacity to makepredictions of radical scavenging activity for unknown substances.

Acknowledgement

The authors acknowledge toMODESLAB 1.0 software owners fordelivering a free copy of such program and the Portuguese Fundaãopara a Ciência e a Tecnologia (FCT - Lisboa) (SFRH/BPD/63946/2009)for financial support.

Appendix. Supplementary data

Supplementary data related to this article can be found online atdoi:10.1016/j.ejmech.2011.12.030.

Table 4The contributions of different structural fragments tothe radical scavenging activity by using of Eq. (5).

Fragments Contribution

F1 0.911F2 0.245F3 2.970F4 �0.177F5 �0.353F6 �0.149F7 �0.181F8 1.645F9 1.754F10 2.406F11 0.766F12 1.491F13 0.644F14 0.107F15 �0.429F16 0.082F17 �0.675F18 0.688F19 0.751F20 1.050F21 0.999F22 0.530F23 0.421



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