3d qsar analysis on arylbenzofuran derivatives as

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International Journal of PharmTech ResearchCODEN (USA): IJPRIF ISSN : 0974-4304Vol.4, No.4, pp 1691-1702, Oct-Dec 2012

3D QSAR Analysis on Arylbenzofuran derivatives asHistamine H3 Receptor Inhibitors using k Nearest

Neighbor Molecular Field Analysis (KNNMFA)

Sanmati K. Jain* and Sudip Nag

*SLT Institute of Pharmaceutical Sciences, Guru Ghasidas Vishwavidyalaya

(Central University), Bilaspur (Chhattisgarh)-495009, India.

*Corres. author: sanmat i jain72@yahoo .co. inPho ne No . 07752-260027, Mob: +919827088484

Abstract: Three dimensional quantitative structure activity relationship (3D QSAR) analysis using knearest neighbor molecular field analysis (kNN MFA) method was performed on a series of arylbenzofuran

derivatives as histamine h3 receptor inhibitors using molecular design suite (VLifeMDS). This study was

performed with 58 compounds (data set) using sphere exclusion (SE) algorithm and random selection

method for the division of the data set into training and test set. kNN-MFA methodology with step wisevariable selection forward-backward, Simulated Annealing and Genetic Algorithms methods were used for

building the QSAR models. Two predictive models were generated with SW-kNN MFA. The most

predictive model was generated by kNN (stepwise forward backward) using sphere exclusion data selection

method. This model explains good internal (q2

= 0.5445) as well as very good external (Predr2

= 0.9013)

predictive power of the model. The steric descriptors at the grid points, S_2307 and S_2377 plays important

role in imparting activity. This model indicates that two steric descriptors are involved. The kNN-MFA

contour plots provided further understanding of the relationship between structural features of substituted

arylbenzofuran derivatives and their activities which should be applicable to design newer potential

H3 receptor inhibitors.

Keywords: 3D-QSAR, kNN-MFA, h3 receptor inhibitors, arylbenzofuran derivatives.

INTRODUCTION

Histamine is a biogenic amine that influences a

wide range of pathophysiological processes. At

present, four subtypes of histamine G protein-

coupled receptors (GPCRs) are known. H1 and H2receptors are concerned in allergic responses and

gastric acid secretion, respectively. The most

recently discovered H4 receptor is mainly located

on mast cells, eosinophils and lymphoid tissues

and seems to be involved in inflammatory

processes (1).

The histamine H3 receptor was identified in 1983

and was initially described as an autoreceptor,

mainly expressed in the central nervous system

(CNS), regulating histamine biosynthesis and

release from histaminergic neurons (2). Histamine

H3 receptors are expressed in the central nervous

system and to a lesser extent the peripheral

nervous system, where they act as autoreceptors in

presynaptic histaminergic neurons, and also

control histamine turnover by feedback inhibition

of histamine synthesis and release (3).

Subsequently, H3 receptors have also been shown

to act as heteroreceptors on non-histaminergic

neurons, where they inhibit the release of other

neurotransmitters such as acetylcholine, dopamine,

norepinephrine, serotonin and various

neuropeptides (4-9).The high density of H3 receptors in different CNS

areas and their influence on the release of a large

variety of neurotransmitters encouraged wide

pharmacological investigation on their

physiological role and the quest for potential


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Sanmati K. Jain e t a l/Int.J.PharmTech Res.2012,4(4) 1692

therapeutic applications of H3-antagonists in the

treatment of various CNS diseases. Among them,

the most promising ones include attention-deficit

hyperactivity disorders (ADHD), Alzheimersdisease, epilepsy, schizophrenia, obesity and

eating disorders (10-19).Since the discovery of the reference antagonist

thioperamide (20), many classes of potent and

selective H3-antagonists have been reported. The

earliest generation of H3-antagonists was derived

from the endogenous neurotransmitter histamine

and the compounds contained an imidazole ring in

their structures (21,22).

The imidazole-based H3 antagonists such as

ciproxifan, thioperamide are useful tools and

reference standards for pharmacological research,

but they have two drawbacks, which includes:

i) Imidazole-based analogues have poor CNS

penetration (23,24) while low CNS penetration

may be a desirable property in a drug targeted

to the treatment of peripheral diseases.

ii) Inhibit cytochrome-P450 (CYP) drug

metabolizing enzymes, leading to drug-drug

interactions by inhibiting the metabolism of

co-administered drugs (25-29).

iii) Additionally, drugs that inhibit CYP enzymes

have the potential to alter the endogenous

metabolism of important circulating

hormones.For this reason non-imidazoles have been targeted

as potential drug candidates, and several distinct

classes of non-imidazole H3 antagonists (30-36)

have been produced. The increasing interest in the

therapeutic potential of H3 antagonists are driving

current medicinal chemistry efforts to identify

potent, selective, therapeutically efficacious and

safe agents for clinical development.

QSAR analysis is done by using the softwareQSARPlus, a product of VLIfe SciencesTechonologies Pvt. Ltd. Pune (37). The

Quantitative Structure Activity Relationship(QSAR) paradigm is based on the assumption thatthere is an underlying relationship between themolecular structure and biological activity. On thisassumption QSAR attempts to establish acorrelation between various molecular propertiesof a set of molecules with their experimentallyknown biological activity.There are two main objectives for the development

of QSAR:

1) Development of predictive and robust QSAR,

with a specified chemical domain, for

prediction of activity of untested molecules.2) It acts as an informative tool by extracting

significant patterns in descriptors related to the

measured biological activity leading to

understanding of mechanisms of given

biological activity. This could help in

suggesting design of novel molecules with

improved activity profile.

Arylbenzofuran, nucleus containing drugs are one

of the most important H3-receptor antagonist,

which are non-imidazole containing drugs. In the

present QSAR study a series of arylbenzofuran

analogues were selected, they differ in

substituents the phenyl ring, and benzofurannucleus. Binding affinities (Ki) of substituted

arylbenzofurans at rat cortical H3-receptors and

cloned human H3-receptors are reported, values

reported are in nanomolar units. The research is

going on this class of selective inhibitors of

Histamine H3-receptor, where some novel agents

has to be explored which will having good potency

to selectively inhibits the histamine H3-receptor, as

well as very less side effects and toxicity.

EXPERIMENTAL

The Work Station: Workstations are raster

systems in which a computer with a full operating

system and mass storage facility is integrated with

graphical display. All the computational methods

were performed on HCL PC using QSARPlus

version 1.0.

Data sets: In the present study 58 molecules ofarylbenzofuran derivatives were used which were

reported to have Histamine H3-receptor antagonist

activity (38). The activity data hH3 binding

affinity (M), determined by using human and rat

histamine H3-receptor, activity data hH3 have

been converted to the logarithmic scale pKi

(moles) and then used for subsequent QSAR

analysis as the response variables. pKi data saved

as.txt.file. The various substituents of allcompounds along with their actual biological

activities are shown in Table 1.

Methodology: Structure of the molecule drawn in

the 2DDrawapp option in Tool menu of

QSARPlus. Then 2D structures where exported to

QSARPlus window (2D structure converted to 3D

structure). After the conversion, structures are

saved as .mol2 file in QSARPlus 3D window.

Energy minimization is done by using Merck

molecular force field (MMFF) method which

results in the optimization of the geometry of themolecule using the following criteria.


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Force fieldMMFFChargeMMFFMax. no. of cycles10,000Convergence criteria (rms gradients)0.01

Dielectric properties

*Distance dependent function

*Constant1.0*Gradient type analysis1.0

Non-bonded cut off: Electro static20.00; VdW10.00.

Models were generated by k-NN-MFA in

conjunction with stepwise (SW) forward-

backward [Cross correlation0.5; Term selection q2; Variance cut off 0.0; No. of Max.

Neighbours 5; No. of Min. Neighbours 2;Select prediction method Distance basedweighted average], Simulated Annealing

[Maximum temperature-100.0; Minimum

temperature-0.01; Decrease temperature by-10.0;

Iteration at given temperature-5; Terms in model-

4; Pertubation limit-5; Cross correlation limit-1.0;

Term selection criteria-q2; Seed-0; Scaling-

Autoscaling; No. of Max. Neighbours 5; No. ofMin. Neighbours 2; Select prediction method Distance based weighted average] and Genetic

Algorithms [Cross correlation limit-1.0; Cross

over probability-0.9; Mutation probability-0.1;

Population-10; Number of generations-1000; Print

after iterations-100; Convergence ending criteria-

0; Chromosome length-3; Seed-0; Term selection

criteria-q2; Scaling-Autoscaling; No. of Max.Neighbours 5; No. of Min. Neighbours 2;Select prediction method Distance basedweighted average] variable selection methods.

VLife Molecular Design Suite38

(VLifeMDS),

allows user to choose probe, grid size, and grid

interval for the generation of descriptors. The

variable selection methods along with the

corresponding parameters are allowed to be

chosen, and optimum models are generated by

maximizing q2. k-nearest neighbor molecular field

analysis (kNN-MFA) requires suitable alignment

of given set of molecules. This is followed bygeneration of a common rectangular grid around

the molecules. The steric, hydrophobic and

electrostatic interaction energies are computed at

the lattice points of the grid using a methyl probe

of charge +1. These interaction energy values are

considered for relationship generation and utilized

as descriptors to decide nearness between

molecules. The term descriptor is utilized in the

following discussion to indicate field values at the

lattice points. The optimal training and test sets

were generated using the random selection

method. This algorithm allows the construction oftraining sets covering descriptor space occupied by

representative points. Once the training and test

sets were generated, kNN methodology was

applied to the descriptors generated over the grid.

Molecular Allignment: In 3D QSAR first the

alignment of the optimized molecules is done by

using Template based alignment method.

Model development: The activity data were

subjected to k-nearest neighbor molecular field

analysis method for model building.

Data selection: Biological activity taken as

dependent variable and descriptors as independent

variable. Following methods were used for

creation of training and test set.

Random selection method

Sphere Exclusion method

Random selection: In order to construct and

validate the QSAR models, both internally andexternally, the data sets were divided into training

85%-75% (85%, 80% and 75%) of total data set

and test sets 15%-25% (15%, 20% and 25%) of

total data set in a random manner. 05 trials were

run in each case.

Sphere Exclusion method: In this method initially

data set were divided into training and test set

using sphere exclusion method. In this method

dissimilarity value provides an idea to handle

training and test set size. It needs to be adjusted by

trial and error until a desired division of trainingand test set is achieved. Increase in dissimilarity

value results in increase in number of molecules in

the test set.

After the creation of training and test set, Min and

Max value of the test and training set is checked,

using the QSAR tool, if the values are not

following the Min Max, then the training / testset is again set and procedure is repeated. If the

Min Max is following, then the k nearestneighbor molecular field analysis method was

used for model building.


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Table 1: General structure of the compounds of Arylbenzofuran derivatives and their biological

activities (data set of 58 molecules)

ON

R1

R

S.

No.

Compound Benzofuran substituent

(R)

Phenyl Substituent

(R1)

hH3(nM) log (1/

hH3)

1 2 H 4-CN 0.45 9.347

2 4a H 3-CN 0.27 9.569

3 4b H 4-F 3.2 8.495

4 4c H 3-F 2.2 8.658

5 4d H 4-Cl 6.3 8.201

6 4e H 3-Cl 5.0 8.3017 4f H 2-Cl 5.3 8.276

8 4g H 4-CF3 5.7 8.244

9 4h H 3-CF3 3.9 8.409

10 4i H 4-Me 7.9 8.102

11 4j H 3-Me 2.7 8.569

12 4k H 2-Me 4.1 8.387

13 4l H 4-OCF3 7.6 8.119

14 4m H 3-OCF3 11 7.959

15 4n H 4-OMe 9.1 8.041

16 4o H 3-OMe 1.2 8.921

17 4p H 2-OMe 15 7.824

18 4q H 3-Cl,4-Cl 3.6 8.44419 4r H 3-Cl,5-Cl 5.0 8.301

20 4s H 3-Me,4-Me 5.9 8.229

21 4t H 3-Me,5-Me 3.6 8.444

22 4u H 4-COOMe 1.9 8.721

23 4v H 3-C(O)Me 0.084 10.076

24 4w H 4-CH2OH 0.88 9.056

25 4x H 3-CH2OH 0.49 9.310

26 8a H 4-Br 7.7 8.114

27 8b H 4-CN,2-Me 2.0 8.699

28 8c H 4-CN,3-Me 0.28 9.553

29 8d H 4-CN,3-F 0.64 9.194

30 8e 7-F 4-CN 0.43 9.36731 8f 7-Me 4-CN 1.1 8.959

32 8g 6-Me 4-CN 0.77 9.114

33 9 H 3-CH(OH)Me 0.44 9.357

34 10 H 3-C(OH)Me2 0.25 9.602

35 11 H 3-COOH 15 7.824

36 12 H 3-C(O)N(Me)OMe 0.62 9.208

37 13a H 3-C(O)Et 0.23 9.638

38 13b H 3-C(O)CH2CHMe2 0.68 9.168

39 13c H 3-C(O)-(3-F)C6H4 2.6 8.585

40 13d H 3-CHO 0.40 9.398

41 14a H 3-C(=NOH)Me 0.49 9.310

42 14b H 3-(=NOMe)Me 0.42 9.37743 14c H 3-C(=NOEt)Me 3.2 8.495

44 14d H 3-C(=NO-t-Bu)Me 4.3 8.367

45 15a H 4-C(O)-c-Pr 0.26 9.585

46 15b H 3-C(O)-c-Pr 0.21 9.678


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47 16 3-I 4-CN 0.51 9.292

48 17a 3-Cl 4-CN 0.39 9.409

49 17b 3-Cl,6-Cl 4-CN 0.83 9.081

50 18a 3-Br 4-CN 0.29 9.538

51 18b 3-Br 4-CN, 3-Me 1.3 8.886

52 19a 3-Ph 4-CN 21 7.67853 19b 3-(3,5-DiMeC6H3) 4-CN 60 7.222

54 19c 3-(3-Pyridyl) 4-CN 13 7.886

55 19d 3-(2-Furyl) 4-CN 2.8 8.553

56 19e 3-(3-Thienyl) 4-CN 2.1 8.678

57 19f 3-(3(2CHO)thienyl) 4-CN 0.73 9.137

58 20 3-(3(2CH2OH)thienyl) 4-CN 1.9 8.721

Total data set of 58 molecules was used for the present study.

Figure 1: Training and test set data fitness plot for Model-1 (trial-1)

RESULTS AND DISCUSSION

Following statistical parameters were used to

correlate biological activity and molecular

descriptors: n = number of molecules, Vn =

number of descriptors, k = number of nearest

neighbor, df = degree of freedom, r2= coefficient

of determination, q2 = cross validated r2 (by the

leave-one out method), pred_ r2 = r2 for externaltest set, pred_ r2se = coefficient of correlation of

predicted data set. Selecting training and test set

by random selection method, the Unicolumn

statics was performed which is shown in Table 2.

The unicolumn statistical analysis can be

interpreted that the mean and standard deviation

for the training and test sets provide insight into

the relative difference in the mean and point

density distribution of the two sets. The minimum

and maximum values in both the training and test

sets are compared such that the maximum of the

test set should be less than that of the training set.

The minimum of the test set should be greater than

that of the training set, suggesting the interpolative

behavior of the test set (i.e., derived within the

minimummaximum range of the training set)which is prerequisite analysis for further QSAR

study.


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Data fitness plot for training and test set is shown

in Figure 1. kNN-MFA plot is shown in Figure 2

and 3D alignment of the molecules is shown in

Figure 3. Results of 3D-QSAR kNN-MFA

analysis using sphere exclusion and random data

selection methods are shown in Table-2 andTable 4-6 respectively. Result of the most

predictive model generated by kNN (Trial-1,

stepwise forward backward) method using sphere

exclusion data selection method is as follows:

kNN MethodTraining Set Size = 53

Test Set Size = 5

Selected Descriptors:S_2307

S_2377

Statistics:

k Nearest Neighbour= 2

n = 53

Degree of freedom = 50

q2 = 0.5445

q2_se = 0.4220

Predr2 = 0.9013

pred_r2se = 0.1955

Descriptor Range:S_2307 -0.007384 -0.007189

S_2377 -0.00308 -0.002385

This model explains internal (q2

= 0.5445) as well

as very good external (Predr2

= 0.9013) predictive

power of the model. The steric descriptors at the

grid points S_2307 and S_2377 plays important

role in imparting biological activity. This model

indicates that two steric descriptors are involved

suggesting that activity is dominated by steric

interactions. kNN-MFA result plot in which 3D-

alignment of molecules with the important steric

points contributing with model with ranges of

values shown in parenthesis represented in Figure-

2.

Finally, it is hoped that the work presented here

will play an important role in understanding the

relationship of physiochemical parameters with

structure and biological activity. By studying the

QSAR model one can select the suitable

substituent for active compound with maximum

potency.

Figure 2: kNN-MFA stepwise forward backwards result plot: 3D-alignment of molecules with the

important steric points contributing with model with ranges of values shown in parenthesis.


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Table 2: Results of 3D-QSAR analysis using kNN method (sphere exclusion)

kNN resultTRIA

L

DISSIMILARI

TY VALUE*

TEST SET

Stepwise forward-

backward

Simulated Annealing Genetic Algorithms

1 6.50 4f,4q,8a,4t,4l k Nearest

Neighbour= 2n = 53

Degree of freedom =

50

q2 = 0.5445

q2_se = 0.4220Predr2 = 0.9013

pred_r2se = 0.1955

k Nearest

Neighbour= 4n = 53

Degree of freedom =

48

q2 = 0.0894

q2_se = 0.5967Predr2 = -0.0971

pred_r2se = 0.6520

k Nearest Neighbour=

4n = 53

Degree of freedom =

49

q2 = 0.0816

q2_se = 0.5993Predr2 = 0.4961

pred_r2se = 0.4419

2 7.00 4f,4q,8a,18b,4t,4x,4l

k Nearest

Neighbour= 2

n = 51Degree of freedom =

47q2 = 0.5601

q2_se = 0.4203

Predr2 = 0.4191pred_r2se = 0.4119

k Nearest

Neighbour= 3


46q2 = 0.1898

q2_se = 0.5704

Predr2 = 0.3218pred_r2se = 0.4451


4


47q2 = 0.0627

q2_se = 0.6135

Predr2 = 0.5667pred_r2se = 0.3558

3 8.00 4d,4f,4o,4q,8a,4t,4x,4g,8c

k Nearest

Neighbour= 2

n = 49

Degree of freedom =

47q2 = 0.3541

q2_se = 0.5096

Predr2 = -0.3366pred_r2se = 0.6471

k Nearest

Neighbour= 4

n = 49

Degree of freedom =

44q2 = 0.3644

q2_se = 0.5055

Predr2 = 0.4201pred_r2se = 0.4262


5

n = 49

Degree of freedom =

45q2 = 0.0799

q2_se = 0.6083

Predr2 = 0.1339pred_r2se = 0.5209

4 8.50 4e,4d,4o,8a,4n,8f,4x,4g,8d,8c,4t,19a

k Nearest

Neighbour= 2

n = 46

Degree of freedom =

43q2 = 0.4779

q2_se = 0.4496

Predr2 = 0.1840

pred_r2se = 0.5766

k Nearest

Neighbour= 2

n = 46

Degree of freedom =

41q2 = 0.1198

q2_se = 0.5837

Predr2 = -0.1785

pred_r2se = 0.6929


3

n = 46

Degree of freedom =

42q2 = 0.1329

q2_se = 0.5794

Predr2 = 0.3848

pred_r2se = 0.5006

5 9.00 4e,4d,4o,8a,18a,4n

,8f,16,4x,4m,8d,8c,8g,4t,4c,2,41,19c

k Nearest

Neighbour= 2n = 40Degree of freedom =

37

q2 = 0.4292

q2_se = 0.4808

Predr2 = 0.0211

pred_r2se = 0.5876

k Nearest

Neighbour= 4n = 40Degree of freedom =

35

q2 = 0.1759

q2_se = 0.5777

Predr2 = -0.3135

pred_r2se = 0.6806


5n = 40Degree of freedom =

36

q2 = 0.0383

q2_se = 0.6241

Predr2 = 0.2357

pred_r2se = 0.5192

*Sphere exclusion data selection method was used in this study


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Table 3: Min-Max table for model (Trial-1, stepwise forward backward)

Table 4: Results of 3D-QSAR analysis using Random selection method (85%)

kNN resultTRIA

L

TEST SET

Stepwise forward-

backward


1 14c,17a,4l,4q,4r,4w,8b,8g,9

k Nearest Neighbour= 2n = 49


q2 = 0.6321

q2_se = 0.3913Predr2 = 0.4170

pred_r2se = 0.3606



q2 = 0.1560

q2_se = 0.5926Predr2 = 0.5958

pred_r2se = 0.3002



q2 = -0.1168

q2_se = 0.6817Predr2 = -0.5758

pred_r2se = 0.5928

2 13a,13d,14a,19a,19d,4r,8a,8e,9



q2 = 0.4442

q2_se = 0.4529Predr2 = -0.3759

pred_r2se = 0.8353



q2 = 0.0829

q2_se = 0.5818Predr2 = 0.0176

pred_r2se = 0.7058



q2 = -0.1484

q2_se = 0.6510Predr2 = 0.2088

pred_r2se = 0.6334

3 13d,14c,19d,4d,4j,4

m,4p,4r,9


n = 49

Degree of freedom = 43q2 = 0.7018q2_se = 0.3404

Predr2 = -0.2799pred_r2se = 0.7189


n = 49

Degree of freedom = 44q2 = 0.1911

q2_se = 0.5606

Predr2 = 0.1789pred_r2se = 0.5758


n = 49

Degree of freedom = 45q2 = -0.0692

q2_se = 0.6446

Predr2 = 0.0974pred_r2se = 0.6037

4 12,14d,18a,4c,4g,4i,

4o,4t,9


n = 49


q2 = 0.4627

q2_se = 0.4685

Predr2 = -0.6210pred_r2se = 0.6606


n = 49


q2 = 0.0950

q2_se = 0.6080

Predr2 = -0.8115pred_r2se = 0.6983


n = 49


q2 = -0.1504

q2_se = 0.6855

Predr2 = 0.4306pred_r2se = 0.3915

5 15b,17a,18a,19d,4d,4q,8c,8e,9



q2 = 0.4752

q2_se = 0.4429Predr2 = -0.6089

pred_r2se = 0.9035



q2 = 0.0775

q2_se = 0.5872Predr2 = 0.3282

pred_r2se = 0.5839



q2 = -0.0979

q2_se = 0.6406Predr2 = -0.0840

pred_r2se = 0.7416

Column

Name

Average Max Min StdDev Sum

Training set 8.8166 10.0760 7.2220 0.6253 467.2790

Test set 8.2794 8.4440 8.1140 0.1638 41.3970


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kNN resultTRIA

L

TEST SET

Stepwise forward-

backward


1 12,14a,14d,17a,19a,19f,4f,4j,4k,4r,8d,9



q2 = 0.2104

q2_se = 0.5651

Predr2 = -0.6242

pred_r2se = 0.7247



q2 = 0.1169

q2_se = 0.5977

Predr2 = -0.6818

pred_r2se = 0.7374



q2 = -0.1244

q2_se = 0.6744

Predr2 = 0.2526

pred_r2se = 0.4916

2 14c,17a,19e,4g,4l,4q,4

r,4w,8b,8e,8g,9


n = 46


q2 = 0.2400

q2_se = 0.5714

Predr2 = -0.2694

pred_r2se = 0.5290


n = 46


q2 = 0.0198

q2_se = 0.6489

Predr2 = 0.3838

pred_r2se = 0.3686


n = 46


q2 = -0.1119

q2_se = 0.6911

Predr2 = -0.8464

pred_r2se = 0.6380

3 12,13a,13d,14a,19a,19

d,4r,4x,8a,8c,8e,9


n = 46


q2_se = 0.4498

Predr2 = -0.0926

pred_r2se = 0.7353


n = 46


q2_se = 0.5746

Predr2 = -0.2460

pred_r2se = 0.7852


n = 46


q2_se = 0.6571

Predr2 = 0.2237

pred_r2se = 0.6198

4 12,14d,18a,19c,4c,4g,

4i,4o,4r,4s,4t,9


n = 46


q2 = 0.6547

q2_se = 0.3739Predr2 = -0.9512pred_r2se = 0.8038


n = 46


q2 = 0.0646

q2_se = 0.6155Predr2 = 0.2371pred_r2se = 0.5026


n = 46


q2 = -0.1603

q2_se = 0.6855Predr2 = 0.2633pred_r2se = 0.4939

5 10,14b,17a,18b,4c,4d,

4m,4o,4t,4x,8d,9


n = 46


q2 = 0.5952

q2_se = 0.4051

Predr2 = -0.9659pred_r2se = 0.8057


n = 46


q2 = 0.1104

q2_se = 0.6006

Predr2 = -0.0129pred_r2se = 0.5783


n = 46


q2 = -0.1540

q2_se = 0.6840

Predr2 = 0.1696pred_r2se = 0.5236

Figure 3: 3D-alignment of molecules.


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kNN resultTRIA

L

TEST SET

Stepwise forward-

backward


1 14c,17a,19e,4g,4i,4j,4l,4

q,4r,4s,4w,8b,8e,8g,9


n = 43Degree of freedom = 41

q2 = 0.2249

q2_se = 0.5844

Predr2 = -0.0006pred_r2se = 0.4862



q2 = 0.0497

q2_se = 0.6471

Predr2 = -0.4015pred_r2se = 0.5755



q2 = -0.1349

q2_se = 0.7072

Predr2 = -0.6110pred_r2se = 0.6170

2 12,14d,17b,18a,18b,19c,4c,4g,4i,4o,4r,4s,4t,8e,9



q2 = 0.6428

q2_se = 0.3893

Predr2 = -0.9495

pred_r2se = 0.7431



q2 = 0.0871q2_se = 0.6224

Predr2 = -0.7086

pred_r2se = 0.6957



q2 = -0.1350q2_se = 0.6940

Predr2 = -0.1094

pred_r2se = 0.5605

3 10,14b,15a,17a,18b,4c,4

d,4m,4o,4t,4x,8a,8d,8f,9


n = 43


q2_se = 0.4760

Predr2 = 0.0867

pred_r2se = 0.5637


n = 43


q2_se = 0.5784

Predr2 = 0.0557

pred_r2se = 0.5732


n = 43


q2_se = 0.6997

Predr2 = 0.0663

pred_r2se = 0.5700

4 10,13d,14a,14b,14d,19c,

20,4c,4d,4k,4q,4w,8b,8d,9



q2 = 0.6145

q2_se = 0.4046Predr2 = -0.8133

pred_r2se = 0.7161



q2 = 0.1529

q2_se = 0.5998Predr2 = -0.4238

pred_r2se = 0.6345



q2 = -0.1799

q2_se = 0.7079Predr2 = 0.1903

pred_r2se = 0.4785

5 11,13c,15a,17a,18b,19a,

19d,19e,4b,4d,4f,4j,4n,8

c,9


n = 43


q2 = 0.5015q2_se = 0.4390

Predr2 = -0.5217

pred_r2se = 0.7820


n = 43


q2 = 0.0915q2_se = 0.5927

Predr2 = -0.1297

pred_r2se = 0.6738


n = 43


q2 = -0.0324q2_se = 0.6318

Predr2 = 0.3867

pred_r2se = 0.4965

CONCLUSION

Three dimensional quantitative structure activityrelationship (3D QSAR) analysis using k nearest

neighbor molecular field analysis (kNN MFA)

method was performed on a series of

arylbenzofuran derivatives as histamine

h3 receptor inhibitors using molecular design suite

(VLifeMDS). This study was performed with 58

compounds (data set) using sphere exclusion (SE)

algorithm and random selection method for the

division of the data set into training and test set.

kNN-MFA methodology with step wise variable

selection forward-backward, Simulated Annealing

and Genetic Algorithms methods were used forbuilding the QSAR models. Two predictive

models were generated with SW-kNN MFA. The

most predictive model was generated by kNN

(stepwise forward backward) using sphere

exclusion data selection method. This model

explains good internal (q2

= 0.5445) as well as

very good external (Predr2 = 0.9013) predictive

power of the model. The steric descriptors at the

grid points, S_2307 and S_2377 plays important

role in imparting activity. This model indicates

that two steric descriptors are involved. The kNN-

MFA contour plots provided further understanding

of the relationship between structural features of

substituted arylbenzofuran derivatives and their

activities which should be applicable to design

newer potential H3 receptor inhibitors.

ACKNOWLEDGEMENTThe authors would like to thanks Head, SLT

Institute of Pharmaceutical sciences, Guru

Ghasidas University, Bilaspur (CG) for providing

the necessary facilities.


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3d qsar analysis on arylbenzofuran derivatives as

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