3d-qsar studies on 2- arylcarbonyl -3-trifluoromethylquinoxaline 1, 4-di-n- oxide derivatives and...

8/3/2019 3d-Qsar Studies on 2- Arylcarbonyl -3-Trifluoromethylquinoxaline 1, 4-Di-n- Oxide Derivatives and Their Reduced An

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Publication Ref No.: IJPRD/2010/PUB/ARTI/VOV-2/ISSUE-6/AUG/008 ISSN 0974 9446

International Journal of Pharma Research and Development Online

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3D-QSAR STUDIES ON 2- ARYLCARBONYL -3-TRIFLUOROMETHYLQUINOXALINE 1, 4-DI- N -OXIDE DERIVATIVES AND THEIR REDUCED ANALOGUES USING

K-NN MFA APPROACH

Joohee Pradhan 1*, Dr. Rajesh Sharma 2,Dr. Anju Goyal 3

1Geetanjali Institute of Pharmacy,Dabok,Udaipur, Rajasthan, India-313022

2School of Pharmacy, Devi Ahilya Vishwavidyalaya,Takshshila Campus, Khandwa Road,

Indore, M.P., India-452 0173B.N Girls College of Pharmacy, Sewashram Road,

Udaipur, Rajasthan, India-313001Email: [email protected]

ABSTRACT

A set of thirty three Quinoxaline derivatives with cytotoxic activities against nine types oftumoral subgroups was subjected to three dimensional quantitative structure activity relationshipstudies through recently introduced k- nearest neighbor molecular field analysis with step wise,simulated annealing and genetic algorithm as variable selection methods resulting in eightstatistically significant models for leukemia, nonsmall lung, colon, CNS, ovarian, prostate andbreast cancers. These models gave a value of q 2 as high as 0.9122 for model 1 and value ofpred_r 2 as high as 0.6738 for model 3 .The k-NN MFA contour plots provided further understandingof the relationship between the structural features of quinoxaline derivatives and their activities,which should be applicable to design new, potential cytotoxic agents.

Key Words: 3D-QSAR, k-NN MFA, Quinoxaline derivatives, Anticancer.

INTRODUCTIONThe search of anticancer compounds hasalways been on the desk top of molecularmodeling and drug design specialists. In

spite of this intensive search, the discoveryof selective anti cancer compounds hasremained a largely illusive goal of cancerresearch. Subsequently, new approaches

Joohee Pradhan


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are needed in order to make an efficientsearch for candidates to be assayed as anticancer drugs [1-6].

Quinoxaline derivatives are a class ofsubstances possessing a broad spectrum ofpharmacological activities includinganticancer [7]. A series of thirty threequinoxaline derivatives (Fig. 1) having goodactivities against leukemia, nonsmall lung,colon, CNS, melanoma, ovarian, renal,prostate and breast cancers were subjectedto three dimensional quantitative structureactivity relationship (3D-QSAR) studies inorder to find new, potent anticancer agents.

Resent trends in 2D/3D-QSAR havefocused on the development of procedurethat allows selection of optimal variablesfrom the pool of descriptors of chemicalstructures i.e. ones that are most meaningfuland statistically significant in terms ofcorrelation with biological activity. This isaccomplished by combining one of thestochastic search methods such as SA,GAs, or evolutionary algorithms with thecorrelation methods such as MLR, PLSR, or

artificial neural networks[8-13]

. The k- nearestneighbor molecular field analysis (k-NNMFA), used for 3D-QSAR analysis of thepresent data set adopts a k-nearestneighbor principle for generatingrelationships of molecular fields with theexperimentally reported activity. Thevariables and optimal k values were chosenusing three variable selection methods viz.step wise (SW), simulated annealing (SA),and genetic algorithm (GA). Like many 3D-QSAR methods, k-NN MFA requiressuitable alignment of given set of molecules.This is followed by generation of a commonrectangular grid around the molecules. Thesteric and electrostatic interaction energiesare computed at the lattice points of the gridusing a methyl probe of charge +1. Theseinteraction energy values are considered forrelationship generation and utilized as

descriptors to decide nearness betweenmolecules. The optimal training and testsets are generated using the sphere

exclusion algorithm. This algorithm allowsthe construction of training sets coveringdescriptor space occupied by representativepoints. Once the training and test sets aregenerated, k-NN methodology is applied tothe descriptors generated over the grid [14].

EXPERIMENTAL WORKThe growth inhibitory data of quinoxalinederivatives (Table 1) against nine types oftumor cells were collected from reported

work of Beatriz Solano et.al.[15]

. All thebiological activity data (Gi 50 in M) wereconverted to log Gi 50 (Table 2A and 2B) toreduce skew ness of data set. Themolecular modeling was carried out onCompaq PC having Pentium IV processorand windows XP operating system, usingthe software namely: Molecular DesignSuite supplied by the VLife Sciences, Pune(VLife MDS) [16]. The structures wereconstructed using the 2D draw application

and converted to 3D structures. Energyminimization and geometry optimization wasconducted using Merck Molecular ForceField (MMFF) method with Root MeanSquare (RMS) gradient set to 0.01 Kcal/mol and iteration limit to 10000. Alignment ofall the thirty-three compounds was doneusing template based alignment in MDS; thealigned structures were used for the study.In the template based alignment method, atemplate structure was defined and used asa basis for alignment of a set of molecules.In the present case, benzene nucleus wasthe template used for template-basedalignment, as it was common to allstructures. The alignment of molecules isshown in Fig.2. For calculation of fielddescriptor values, using Tripos force field,both electrostatic and steric field type withcut offs 10.0 and 30.0 Kcal/mol respectively


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were selected and charge type was selectedas Gasteiger Marsili. Dielectric constantwas set to 1.0 considering the distance

dependent dielectric function. Probe settingwas carbon atom with charge 1.0 and gridsetting as follows:

From To IntervalX: - 6.730200 14.603500 2.0000Y: - 7.636500 10.074900 2.0000Z: - 12.37640 8.074400 2.0000

This resulted in calculation of 2080 fielddescriptors (1040 for each electrostatic andsteric) for all the compounds in separatecolumns. For performing QSAR analysis, allthe invariable columns were removed fromthe work sheet, as they do not contribute toQSAR. The optimal test and training dataset were generated using sphere exclusionmethod. The dissimilarity level was set to 5,as the higher the dissimilarity level, thelesser the predictive ability of QSAR model.The method resulted in selection of eightcompounds as test set and remaining othersas training set.Building k-NN MFA ModelsSince there was a large pool of descriptorsavailable to build model, various variableselection methods viz. stepwise variableselection, simulated annealing, and geneticalgorithm were used along with k-nearestneighborhood (k-NN) to find optimal sub-setof descriptors for k-NN MFA model.Step-wise variable selection method

The k-NN MFA models were developedusing step-wise forward-backward methodwith cross correlation limit set to 0.5 andterm selection criteria as q 2. F-test in wasset to 4.0 and F-test out to 3.99. As someadditional parameters, variance cut-off wasset as 2 Kcal/mol and scaling and autoscaling, additionally the k-nearest neighborparameter setting was done within the range

of 2-5 and prediction method was selectedas distance based weighted average.Simulated annealing variable selection

methodThe cross correlation limit was set as 0.5,terms in model as 4, maximum temperatureas 100 0C, min temperature as 0.01,iteration at given temperature as 5,decrease temperature by as 10, seed as 0,perturbation limit as 1, and term selectioncriteria as q 2. Rests of the settings were asdescribed in stepwise variable selectionmethod.Genetic algorithm variable selection

methodCross correlation limit as 0.5, crossoverprobability as 0.95, Mutation probability as0.05, term selection criteria as q 2, populationas 10, number of generations 10000, printafter iterations: 100, seed as 0.Convergence criteria 0.01, convergenceending criteria: 5 and chromosome length as3. Additional parameter settings were asdescribed in step-wise variable selectionmethod.

Among the various models generated for theselected members of training and test sets,8 statistically significant models wereidentified. The software VLife MDS allowsuser to choose probe, grid size, and gridinterval for the generation of descriptors.The variable selection methods along withthe corresponding parameters are allowedto be chosen and optimum models aregenerated by maximizing q 2. The fitnessplots for the entire statistically significantmodels viz. model 1 to 8 are given in Fig.3and corresponding observed and predictedactivities are depicted in Tables 3A and 3B.The 3D plots included in Fig.4 shows theelectrostatic and steric fields important forinteraction of molecules with space, andthus modification of these fields according tothe ranges obtained may result into morepotent compounds.


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RESULTS AND DISCUSSION

The k-NN MFA of quinoxaline derivativeswith reported cytotoxic activities against allthe nine types of tumor cells using SW, SA,and GA variable selection methods resultedin several statistically significant models, ofwhich corresponding best models arereported herein. The model selection criteriabeing the value of q 2, the internal predictiveability of the model, and that of pred_r2, theability of model to predict the activity ofexternal test set. For activity against

leukemia cell lines, model 1 generated withSW variable selection method, found to bestatistically most significant especially withrespect to internal Predictive ability(q2=0.9122) of the model. The modelshowed an external predictive ability ofabout 50 %( pred_r2=0.4826).

Model 1: Model for leukemia

S_424 (-0.7613 to 30.0000);

E_350(0.4939 to 10.0000);E_732 (-1.6457 to -6.1262);S_774 (-0.1099 to -0.0814)q2 = 0.9122; q2_se = 0.3034;Pred_r2 = 0.4826 ;pred_r2se = 0.7773

Another statistically significant model model2 was obtained for cytotoxic activity againstnonsmall lung through SW k-NN MFA

justified by internal and external Predictiveability of the model as 87% (q 2=0.8696) and43% (pred_r 2=0.4357) respectively.

Model 2: Model for Non small lungcancerS_425 (1.6809 to 14.9994); E_566 (-4.1910to 4.4985)

q2 = 0.8696 ;q2_se = 0.2707 Predr2 =0.4357 ;pred_r2se = 0.6130

For colon cancer cells, Model 3 had a valueof q 2=0.8008 and that of pred_r 2=0.6738that explained 80% of total variance (internalPredictive ability) and 67% predictive powerfor the external test set.

Model 3: Model for colon cancerS_647 (-0.7403 to -0.5645);S_481 (-0.6315 to -0.5299)q2 = 0.8008; q2_se = 0.3954 Pred_r2 =0.6738; pred_r2se = 0.4552

Another statistically significant model, model4 was generated for activity against CNScancer cells having a value of q 2=0.8480and that of pred_r 2=0.4054.

Model 4: Model for CNS cancerS_425 (1.6809 to 1.9651); E_262 (-10.000to -3.4808)q2 = 0.8480; q2_se = 0.2711 Pred_r2 =

0.4054; pred_r2se = 0.5683Model 5 and model 6 with values of q20.8969 and 0.8622 also proved theirstatistical significance supported by fairlywell values of pred_r2 (0.3740 and 0.3371respectively).

Model 5 and 6: Models for ovarian cancer Model 5 S_424 (-0.5352 to 30.0000); E_262 (-10.0000 to 3.7342)

q2 = 0.8969; q2_se = 0.2395 Pred_r2 =0.3470; pred_r2se = 0.4966Model 6S_481 (-0.6315 to 30.0000); E_251 (-10.0000 to -6.4378)q2 = 0.8622 ;q2_se = 0.2769 Predr_2 =0.3371; pred_r2se = 0.5003


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The measure of goodness of internalPredictive ability q 2 also allowed mentioninghere model 7 and 8. Model 8C developed

through SA k-NN MFA for cytotoxic activityagainst prostate cells was having internalPredictive ability of 77% (q 2=0.7721) and59% (pred_r 2=0.5950) respectively.Model 7: Model for prostate cancerS_424 (-0.5352 to -0.5197); E_163 (-3.3142to 0.2325)q2 = 0.7712; q2_se = 0.4124 Predr2 =0.5950; pred_r2se = 0.3999The last significant model, model 8 wasselected as model for activity against breast

cancer as justified by value of q2

=0.7882and that of pred_r 2=0.5728.Model 8 : Model for breast cancerS_647 (-0.7403 to -0.5645) ; S_481 (-0.6315 to -0.5299)q2 = 0.7882; q2_se = 0.3607 Predr2 =0.5728; pred_r2se = 0.4250The k-NN MFA contour plots which showedthe relative position and ranges of thecorresponding important electrostatic/stericfields in the model provided guidelines for

new molecule design. A negative rangeindicated that negative electrostatic potentialwas favorable for increase in the activity andhence a more electro negative substituentgroup was preferred in that region.Conversely, a Positive range indicated thatpositive electrostatic potential was favorablefor increase in the activity and hence a lesselectronegative substituent group waspreferred in that region.As far as steric field is concerned, negative

range indicated that negative steric potentialwas favorable for increase in the activity andhence less bulky substituent group waspreferred in that region. A positive rangeindicated that positive steric potential wasfavorable for increase in the activity andhence more bulky substituent group waspreferred in that region. A deep insight intothe resulted models revealed that in almost

all the models, both electrostatic and stericdescriptors are equally important(contribution 50:50) in describing the

biological activity of the quinoxalinederivatives, except for activity against colonand breast cancer cells, where only stericdescriptors play the role.For activity against leukemia,electronegative sustituents at Ra/Rb alongwith furyl or thienyl group at position A mayresult into more active compounds. A lessactivity of compounds 5, 10, 11, 15, and 24further proves the fact.In order to find potent and selective agent

against non small lung cancer, a deepinsight into model 2 revealed that stericallybulky group is favored at position A with amore flexible range of substitution atposition 3. The thienyl group withoutsubstitution at position Ra and Rb would befavorable for activity as is evident fromcompound 17, having maximum activityagainst non small lung cancer cells. Inaddition, a methyl group at position Ra/Rbwould also be beneficial.

For activity against colon cancer, model 3having electrostatic and steric contributionas 66.6 and 33.3 percent respectively,allows less bulky substituents to bepreferred at position A and less electronegativity is required at position 4.Consequently, naphthyl ring at position Aand reduced compounds at position 1 and 4would be detrimental for the activity.Further, a more electro negative substituentat Ra and Rb and a more bulky group at Awould be required for activity against CNScancer cells; methoxy group at Rb isdiscouraged.Models 5 and 6 for ovarian cancerincorporate both steric and electrostaticdescriptors equally where more flexibility isallowed sterically, while more electronegative substitution at Ra and/or Rb wouldbe beneficial for the activity supported by


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the fact that compound 17 without anysubstitution at these positions shows noactivity against prostate cancer. Thienyl

group at position A is again preferred foractivity. As far as activity against breastcancer is concerned, only steric factordecides the specificity where a less bulkysubstitution at position 4 would be beneficialwith phenyl or naphthyl ring discouraged atposition A.

CONCLUSIONS3D-QSAR studies were performed using k-NN MFA on a series of quinoxaline

derivatives with growth inhibitory activityagainst nine type of tumoral subgroup ofcells. Eight statistically significant modelswere obtained with LOO cross validated q 2

values 0.9122, 0.8696, 0.8008, 0.8480,0.8969, 0.8622, 0.7721, 0.7882 for models1to 9 respectively. The developed modelsalso possess promising predictive ability asdiscerned by testing on the external test setand should be useful to elucidate therelationship between compound structure

and biological activities and to facilitatedesign of more potent and selectivecytotoxic agents. For example thienyl orfuryl ring at position A may result in morepotent inhibitors than phenyl, naphthyl, sincethe increase in bulk due to ring at this placewould not fit the ranges so obtained.Similarly, a methoxy substitution at Rb mayalways be unfavorable for cytotoxic activity.

ACKNOWLEDGEMENT The authors are thankful to Head, School ofPharmacy to provide facilities and VlifeScience Technologies Pvt. Ltd, 1, Akshay50, Anand Park, Aundh, Pune, India toprovide the software.

REFERENCES:

1. Estrada E, Uriarte E, Montero A,Teijeria M, Santana L, and Clercq E De, ANovel Approach for the Virtual Screening

and Rational Design of AnticancerCompounds, J. Med. Chem., 43, 2000,1975-1985.2. Denny WA, in The search for anticancerdrugs, M J Warring, B A J Ponder (Eds),Kluwer, Dordrecht, 1992, 320-338.3. Kubinyi H, J. Recept. Signal Transduct.Res, 19, 1999,15-18.4. Walters WP, Stahl MT and Murcko MA,Virtual Screening an overview, DrugDiscov. Today, 3, 1998, 160-178.

5. Ferrante K, Winograd B and Canetta R,Cancer Chemother. Pharmacol., 43, 1999,S61.6. Menta E and Palumbo M, Antineoplasticagents, Expert Opin. Ther Pat., 8, 1998,1627-1672.7. Porter A E A, In ComprehensiveHeterocyclic Chemistry, Pergarmon, NewYork, , 1984, 157-197.8. Sutter J M, Dixon S L and Jurs P C,Automated Descriptor Selection for

Quantitative Structure-Activity RelationshipsUsing Generalized Simulated Annealing,Chem Inf. Comput.Sci.,35,1995,77-84.9. Rogers D and Hopfinger A J, Applicationof Genetic Function Approximation toQuantitative Structure-Activity Relationshipsand Quantitative Structure-PropertyRelationships , J. Chem. Inf. Comput. Sci.,34, 1994, 854-866.10. Kubinyi H, Variable Selection in QSARStudies. I. An Evolutionary Algorithm,Quant.Struct.-Act.Relat . ,13,1994,285-294.11. Kubinyi H, Variable Selection in QSARStudies. II. A Highly Efficient Combination ofSystematic Search and Evolution, Quant.Struct.-Act. Relat., 13, 1994, 393-401.12. Luke B T, Evolutionary ProgrammingApplied to the Development of QuantitativeStructure-Activity Relationships andQuantitative Structure- Property


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Relationships, J. Chem. Inf. Comput. Sci.,34, 1994, 1279-1287.13. So S S and Karplus M, Evolutionary

Optimization in Quantitative Structure-Activity Relationship: An Application ofGenetic Neural Networks, J. Med. Chem.,39, 1994, 1521-1530.14. Ajmani S, Jadhav K and Kulkarni S A,Three Dimensional QSAR using K-NearestNeighbor, Method & Its interpretation, J.Chem. Inf. Model, 46, 2006, 24-31.15. Solano B, Junnotula V R, Marn A,Villar R, Burguete A, Vicente E, Perez-

Silanes S, Aldana I, Monge A, Dutta S,Sarkar U, and Gates KS, Synthesis andBiological Evaluation of New 2-Arylcarbonyl-

3-trifluoromethylquinoxaline1, 4-Di-N-oxideDerivatives and Their Reduced Analogues,J. Med. Chem., 2007, accessed 10 oct2007.16. Vlife MDS software package, version3.0, supplied by Vlife science technologiesPvt. Ltd, 1, Akshay 50, Anand park, Aundh,Pune,India 411007.

Tables and Figures:Table1 Structures of 2 Arylcarbonyl 3-trifluromethylquinoxaline 1, 4 Di N oxide

derivatives and their reduced analogs

Compound Ra Rb A1 H F phenyl2 H CF3 phenyl3 CF3 H phenyl4 F F naphthyl5 Cl Cl naphthyl6 H F naphthyl7 H Cl naphthyl8 H CF3 naphthyl9 CF3 H naphthyl

10 H H thienyl11 CH3 CH3 thienyl12 F F thienyl13 Cl Cl thienyl14 H CH3 thienyl15 H OCH3 thienyl16 H F thienyl17 H Cl thienyl18 H CF3 thienyl

19 CF3 H thienyl20 H H furyl21 F F furyl22 Cl Cl furyl23 H CH3 furyl24 H OCH3 furyl25 H F furyl26 H Cl furyl27 H CF3 furyl


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8

28 CF3 H furyl

N+

N

O

-O

CF3

OF3C

29

N+

N

O

O-

CF3

O

F3C

30

N

N +

OO -

CF 3

OF 3 C31

N

N+

OO-

CF 3

O

F3C

32

N

N

O

CF 3

OF 3 C33

Table 2A Cytotoxic activities (mean Gi 50 M Inhibition Cell Growth and pGi 50) against tumoralsubgroup cell lines of Quinoxaline derivatives

Comp. Leukemia Nonsmalllung Colon CNS MelanomaGi50 pGi 50 Gi50 pGi 50 Gi50 pGi 50 Gi50 pGi 50 Gi50 pGi 50

1 0.23 -0.638 0.80 -0.097 0.54 -0.267 1.45 0.161 0.95 -0.0222 0.24 -0.619 0.64 -0.194 0.46 -0.337 0.69 -0.161 1.13 0.0533 0.77 -0.113 1.66 0.220 1.04 0.017 1.71 0.233 1.23 0.0894 0.77 -0.113 3.47 0.540 1.71 0.233 2.51 0.399 2.50 0.3975 1.58 0.198 1.70 0.100 1.55 0.190 1.63 0.212 1.57 0.1956 0.44 -0.356 1.54 0.187 0.74 -0.130 1.61 0.206 1.12 0.0497 0.65 -0.187 1.51 0.178 1.29 0.110 1.47 0.167 1.23 0.0898 0.26 -0.585 1.42 0.152 0.66 -0.180 0.95 -0.022 0.97 -0.0139 1.20 0.079 1.78 0.250 1.73 0.238 1.72 0.235 1.48 0.170

10 13.93 1.144 0.35 -0.456 0.83 -0.081 1.45 0.161 1.39 0.14311 2.71 0.433 2.81 0.448 5.31 0.725 3.05 0.484 3.99 0.60012 0.05 -1.301 0.83 -0.081 0.27 -0.568 0.44 -0.356 0.39 -0.40813 0.02 -1.699 2.04 0.309 0.59 -0.229 0.67 -0.174 0.94 -0.02614 0.49 -0.309 0.56 -0.252 0.88 -0.055 0.75 -0.125 0.90 -0.04515 4.43 0.646 3.93 0.594 7.08 0.850 6.24 0.795 5.89 0 .77016 0.27 -0.568 1.68 0.225 0.92 -0.036 1.63 0.212 1.51 0.17817 0.07 -1.155 0.07 -1.154 0.04 -1.397 0.09 -1.045 0.12 -0.92018 0.20 -0.699 0.45 -0.346 0.38 -0.420 0.48 -0.318 0.90 -0.04519 0.03 -1.523 0.30 -0.522 0.15 -0.824 0.18 -0.744 0.33 -0.48120 0.07 -1.155 0.35 -0.456 0.51 -0.292 0.40 -0.398 0.67 -0.173

21 0.43 -0.366 0.73 -0.136 0.19 -0.721 0.37 -0.431 0.56 -0.25122 0.27 -0.568 0.28 -0.553 0.50 -0.301 0.79 -0.102 1.57 60.19523 0.26 -0.585 0.47 -0.328 1.17 0.068 0.74 -0.130 0.88 -0.05524 3.96 0.597 3.49 0.542 2.51 0.399 3.20 0.505 3.03 0.48125 0.30 -0.522 3.13 0.495 1.06 0.025 2.35 0.371 1.56 0.19326 0.19 -0.721 0.36 -0.443 0.21 -0.677 0.77 -0.113 0.49 -0.309

27 0.21 -0.677 0.76 -0.1191 0.57 -0.244 0.91 -0.040 1.11 0.045

28 0.13 -0.886 0.48 -0.318 0.28 -0.552 0.81 -0.091 0.86 -0.065

29 51.09 1.70848.14 1.682

41.41 1.617

45.53 1.658

42.90 1.632

30 48.4 1.685 41.9 1.622 75.1 1.875 35.4 1.549 46.1 1.663


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Table 2B Cytotoxic activities (mean Gi 50 M Inhibition Cell Growth and pGi 50) against tumoralsubgroup cell lines of Quinoxaline derivatives

Comp. Ovarian Renal Prostate BreastGi50 pGi50 Gi50 pGi 50 Gi50 pGi 50 Gi50 pGi 50

1 1.34 0.127 1.14 0.056 0.53 -0.275 0.73 -0.1362 0.58 -0.236 0.33 -0.481 0.27 -0.568 0.54 -0.2673 1.18 0.071 1.47 0.167 1.32 0.120 1.41 0.1494 3.80 0.579 2.18 0.338 1.46 0.164 2.36 0.3725 1.51 0.178 1.64 0.214 1.55 0.190 1.62 0.2096 1.20 0.079 1.48 0.170 1.62 0.209 1.70 0.2307 1.20 0.079 1.38 0.139 1.48 0.170 1.15 0.0608 0.47 -0.327 0.58 -0.236 0.57 -0.244 0.99 -0.0049 1.69 0.227 1.55 0.190 1.51 0.178 1.67 0.222

10 0.75 -0.124 0.48 -0.318 0.40 -0.397 0.85 -0.07011 2.34 0.369 4.62 0.664 3.89 0.589 3.08 0.48812 0.53 -0.275 0.50 -0.301 0.51 -0.292 0.41 -0.38713 1.16 0.064 1.60 0.204 0.79 -0.102 0.79 -0.10214 0.27 -0.568 0.25 -0.602 0.89 -0.050 0.56 -0.251

15 4.99 0.698 5.11 0.708 2.48 0.394 4.92 0.69116 1.17 0.068 1.65 0.206 1.51 0.178 1.19 0.07517 0.16 -0.795 0.03 -1.522 - - 0.08 -1.09618 0.43 -0.366 0.31 -0.508 0.20 -0.698 0.50 -0.30119 0.20 -0.698 0.24 -0.619 0.15 -0.823 0.18 -0.74420 1.45 0.161 0.33 -0.481 0.20 -0.698 0.76 -0.11921 0.39 -0.408 0.36 -0.443 0.42 -0.376 0.20 -0.69822 1.65 0.217 0.31 -0.508 0.33 -0.481 1.38 0.13923 0.85 -0.070 0.58 -0.236 0.24 -0.619 0.66 -0.18024 2.51 0.399 1.74 0.240 3.16 0.499 2.88 0.45925 2.03 0.307 2.41 0.382 2.60 0.414 1.33 0.12326 0.56 -0.251 0.20 -0.698 0.11 -0.958 0.28 -0.55227 0.72 -0.142 0.43 -0.366 0.42 -0.376 0.57 -0.244

28 0.65 -0.187 0.49 -0.309 0.19 -0.721 0.48 -0.31829 42.49 1.628 60.60 1.782 51.88 1.715 32.04 1.50530 42.99 1.633 49.69 1.696 35.08 1.545 59.11 1.77131 29.29 1.466 28.59 1.456 38.46 1.585 34.33 1.53532 40.43 1.606 34.57 1.538 44.67 1.650 38.52 1.58533 90.85 1.958 100.00 2.000 100.00 2.000 84.00 1.924

(-) indicates absence of biological activity

2 3 1 8 1

31 88.10 1.94539.41 1.595

49.79 1.697

25.31 1.403

31.52 1.498

3243.65 1.639

47.74 1.679

85.39 1.931

24.45 1.388

80.58 1.906

33 53.95 1.73281.52 1.911

100.0 2.000

75.86 1.880

100.0 2.000


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Table 3A Observed and predicted activities of statistically significant models obtained byk-NN MFA

Model 1 Model 2 Model 3 Model 4Compd. Obs Pred Obs Pred Obs Pred Obs Pred

1 -0.6383 -0.5852 -0.0969 -0.2466 -0.2676 0.017 0.1614 0.1962 -0.6198 -0.6197 -0.1938 -0.0246 -0.3372 0.2142 -0.1612 0.04653 -0.1135 -0.5885 0.2201 0.3494 0.017 -0.2676 0.233 -0.18195 0.1978 -0.1871 0.2304 -0.1699 0.1903 -0.0498 0.2122 -0.39026 -0.3565 -0.3437 0.1875 0.1939 -0.1308 0.1741 0.2068 0.03977 -0.1871 0.1091 0.179 0.2001 0.1106 0.0525 0.1673 0.03978 -0.585 -0.4112 0.1523 0.2122 -0.1805 -0.0735 -0.0223 -0.0968

9 0.0792 0.3258 0.2504 0.0677 0.238 -0.0735 0.2355 0.155410 1.144 0.4035 -0.4559 -0.1541 -0.0809 -0.1083 0.1614 0.195711 0.433 0.8641 0.4487 0.0371 0.7251 0.2341 0.4843 0.430512 -1.301 -1.1363 -0.0809 -0.302 -0.5686 -0.3567 -0.3565 -0.723915 0.6464 0.6771 0.5944 1.6641 0.85 -0.0098 0.7952 0.494116 -0.5686 -0.6338 0.2253 -0.2052 -0.0362 -0.6231 0.2122 -0.138719 -1.5229 -1.042 -0.5229 -0.1608 -0.8239 -0.0887 -0.7447 -0.2918

21 -0.3665 -0.8235 -0.1376 -0.2817 -0.7212 -0.5528 -0.4318 0.0397

23 -0.585 -0.4006 -0.3279 0.0676 0.0682 0.5622 -0.1308 0.001

24 0.5977 0.351 0.5428 0.2876 0.3997 0.3965 0.5051 0.756926 -0.7212 -0.6083 -0.4437 -0.1998 -0.6778 0.078 -0.1135 -0.024827 -0.6778 -0.6515 -0.1192 -0.3623 -0.2441 -0.3805 -0.041 -0.083428 -0.8661 -0.4643 -0.3188 -0.2052 -0.5528 -0.721 -0.0915 -0.073729 1.7083 1.6901 1.6825 1.6508 1.6171 1.8798 1.6583 1.669730 1.665 1.7065 1.6225 1.6152 1.8757 1.6686 1.55 1.388331 1.945 1.5557 1.5956 1.6619 1.6971 1.9637 1.4033 1.7404

32 1.64 1.7153 1.6789 1.6229 1.9314 1.8497 1.3883 1.55

33 1.732 1.6879 1.9112 1.6508 2.000 1.8221 1.88 1.5774

4* -0.1135 -0.1354 -1.1549 0.5403 0.233 0.2146 0.3997 -0.305813* -1.699 -0.8263 -0.5528 0.3096 -0.2291 -0.3007 -0.1739 -0.304314* -0.3098 0.367 -0.4559 -0.2518 -0.0555 0.3964 -0.1249 -0.028117* -1.1549 0.3266 -0.3468 -1.1549 -1.3979 -0.4304 -1.0458 0.017418* -0.699 -0.6067 -0.2518 -0.3468 -0.4202 -0.2079 -0.3188 0.004520* -1.1549 -0.2726 0.3096 -0.4559 -0.2924 -0.3561 -0.3979 0.18622* -0.5686 -0.657 -0.5528 -0.301 -0.3265 -0.4617 -0.1024 -0.297425* -0.5229 -0.6642 0.4955 0.0253 -0.4617 0.2146 0.3711 0.0204

*test set compounds


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Table 3B Observed and predicted activities of statistically significant models obtained byk-NN MFA

Model 5 Model 6 Model 7 Model 8Compd. Obs Pred Obs Pred Obs Pred Obs Pred

1 0.1271 -0.1023 0.1271 0.0466 -0.2757 -0.1951 -0.1367 0.14922 -0.2366 -0.0305 -0.2366 0.082 -0.5686 -0.0462 -0.2627 0.21623 0.0719 -0.3087 0.0719 -0.285 0.1206 -0.5292 0.1492 -0.13675 0.179 -0.0007 0.179 -0.1046 0.1903 0.1846 0.2095 -0.02266 0.0792 -0.3102 0.0792 0.0792 0.2095 -0.3328 0.2304 0.14147 0.0792 0.042 0.0792 0.0679 0.1703 0.1836 0.0607 0.22668 -0.3279 -0.2288 -0.3279 -0.2627 -0.2441 -0.3504 -0.0044 -0.029

9 0.2279 -0.0532 0.2279 -0.3454 0.179 -0.1194 0.2227 -0.02910 -0.1249 -0.0511 -0.1249 0.0895 -0.3979 -0.7596 -0.0706 0.035611 0.3692 0.4733 0.3692 -0.0831 0.58 -0.5068 0.4886 0.139712 -0.2757 -0.1546 -0.2757 -0.2733 -0.2924 -0.1466 -0.3872 -0.238315 0.6981 0.3544 0.6981 0.3976 0.3845 0.5211 0.692 0.145416 0.0682 -0.0903 0.0682 0.0734 0.179 -0.4237 0.0755 -0.469819 -0.699 -0.1586 -0.699 -0.1944 -0.3239 -0.0643 -0.7447 -0.2124

21 -0.4089 -0.2126 -0.4089 -0.2463 -0.3768 -0.1052 -0.699 -0.3178

23 -0.0706 -0.0658 -0.0706 0.1641 -0.6198 -0.6343 -0.1805 0.474

24 0.3997 0.5069 0.3997 0.6935 0.4997 0.4446 0.4594 0.153926 -0.2518 -0.0269 -0.2518 0.0706 -0.9586 -0.4207 -0.5528 0.050527 -0.1427 -0.0478 -0.1427 0.0357 -0.3768 -0.1946 -0.2441 -0.464328 -0.1871 -0.2569 -0.1871 -0.2912 -0.7212 -0.5388 -0.3188 -0.698729 1.6283 1.9583 1.6283 1.6673 1.715 1.713 1.5057 1.776730 1.6334 1.6067 1.6334 1.6543 1.5451 1.6519 1.7717 1.56231 1.4667 1.7089 1.4667 1.6893 1.585 1.8396 1.5357 1.745

32 1.6067 1.6334 1.6067 1.5854 1.65 1.5514 1.5857 1.7315

33 1.9583 1.6283 1.9583 1.5416 2 1.9862 1.9243 1.5623

4* 0.5798 -0.1546 0.5798 -0.1643 -0.337 -0.4304 0.3729 0.216213* 0.0645 0.0206 0.0645 -0.1084 -0.1023 -0.384 -0.1024 -0.312814* -0.5686 -0.0286 -0.5686 -0.0506 -0.0305 -0.3265 -0.2518 0.153817* -0.7959 -0.0286 -0.7959 - - -0.3561 -1.0969 -0.33518* -0.3665 -0.0618 -0.3665 -0.699 -0.5096 -0.3007 -0.301 -0.138520* 0.1614 -0.0505 0.1614 -0.699 -0.1172 0.3964 -0.1192 -0.237822* 0.2175 0.0202 0.2175 -0.4815 -0.3822 -0.4617 0.1399 -0.2325* 0.3075 -0.0506 0.3075 0.415 -0.0792 0.2146 0.1239 -0.399

*test set compounds


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N4

3

2N1

8

7

6

5

A

Rb

Ra

O

CF 3

Fig 1 Parent structure of quinoxaline derivatives.

Fig.2 Template based alignment of Molecules.

Model 1 Model 2 Model 3 Model 4


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Model 5 Model 6 Model 7 Model 8Fig 3 Graphs a of observed v/s predicted activities of Model 1 to 8 Obtained through kNN MFA.

aObserved activity on X-axis; predicted activity on Y-axis; Red dots represent training set

compounds; Blue dots represent test set compounds.

Model 1 Model 2 Model 3 Model 4

Model 5 Model 6 Model 7 Model 8Fig 4 3D Plots of Models 1to 8 th obtained

3d-qsar studies on 2- arylcarbonyl -3-trifluoromethylquinoxaline 1, 4-di-n- oxide derivatives and...

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