3D-QSAR and molecular docking studies on HIV protease inhibitors

Accepted Manuscript 3D-QSAR and molecular docking studies on HIV protease inhibitors Jianbo Tong, Yingji Wu, Min Bai, Pei Zhan PII:

S0022-2860(16)30978-4

DOI:

10.1016/j.molstruc.2016.09.052

Reference:

MOLSTR 22964

To appear in:

Journal of Molecular Structure

Received Date: 13 July 2016 Revised Date:

17 September 2016

Accepted Date: 19 September 2016

Please cite this article as: J. Tong, Y. Wu, M. Bai, P. Zhan, 3D-QSAR and molecular docking studies on HIV protease inhibitors, Journal of Molecular Structure (2016), doi: 10.1016/j.molstruc.2016.09.052. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

ACCEPTED MANUSCRIPT 3D-QSAR and molecular docking studies on HIV protease inhibitors Jianbo Tong∗, Yingji Wu, Min Bai, Pei Zhan College of Chemistry and Chemical Engineering, Shaanxi University of Science & Technology, Xi’an 710021, PR China

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Abstract In order to well understand the chemical-biological interactions governing their activities toward HIV protease activity, QSAR models of 34 cyclic-urea derivatives with inhibitory HIV were developed. The quantitative structure activity relationship (QSAR) model was built by using comparative molecular similarity indices analysis (CoMSIA) technique. And the best CoMSIA model

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has rcv2, rncv2 values of 0.586 and 0.931 for cross-validated and non-cross-validated. The predictive ability of CoMSIA model was further validated by a test set of 7 compounds, giving rpred2 value of

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0.973. Docking studies were used to find the actual conformations of chemicals in active site of HIV protease, as well as the binding mode pattern to the binding site in protease enzyme. The information provided by 3D-QSAR model and molecular docking may lead to a better understanding of the structural requirements of 34 cyclic-urea derivatives and help to design potential anti-HIV protease molecules.

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1 Introduction

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Keywords Cyclic-urea derivatives, 3D-QSAR, CoMSIA, Molecular docking

Acquired immunodeficiency syndrome (AIDS) is one of leading causes of death worldwide, which

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may be induced by Human immunodeficiency virus (HIV) retrovirus [1] and progressive damage the immune system manifesting in serious opportunistic diseases. The millions people living with HIV, and it’s high actively spreading, fatality rate brings large economic and social impacts [2]. Many drugs are getting ineffective due to resistance from the mutation-prone HIV. Hence, there is an urgent need to develop new drugs with better therapeutic effect [3]. The HIV protease receptor (HIV PR) is a key therapeutic target for the development of anti-HIV inhibitors [4] for the treatment of AIDS as it plays an important role in the maturation and replication of the virus. HIV PR is a small enzyme; acting as a

*Correspondence author. Fax: 86-29-86168312; Tel: 86-29-86168315; E-mail address: [email protected](J.B. Tong)

ACCEPTED MANUSCRIPT dimer of two 99-residue subunits; and is tractable for structural and computational analyses. [5] Anti-AIDS drugs are classified into three main categories including nucleoside reverse transcriptase inhibitors, non-nucleoside reverse transcriptase inhibitors and protease inhibitors [6]. HIV protease is an important target in anti-HIV drug therapy [7]. Based on present many crystallographic [8] and

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energetic studies [9], HIV protease has become an attractive target for the computer-aided drug design [10]. Quantitative structure activity relationships (QSARs) is an universal method for chemometrics, which reveals the relationship between chemical structures and their biological activities. As a supportive method for drug design and prediction of drug activity, molecular docking is also applied to

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understand the structural interaction between drug and receptor [11].

Herein, the 3D-QSAR was performed by using the comparative molecular similarity indices analysis

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(CoMSIA) technique [12]. In present studies, CoMSIA was applied to investigating the relationship between structure and activity on inhibiting HIV protease of 34 cyclic-urea derivatives [13]. CoMSIA-based 3D-QSAR study has been carried out on cyclic-urea derivatives to determine the influence of steric, electrostatic, and hydrophobic fields of these compounds on their HIV inhibitory activity. Furthermore, these fields were mapped onto the inhibitors binding pocket of HIV protease for

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the better understanding of these interactions. Seven analogs of cyclic-urea derivatives were designed and synthesized as potential HIV protease inhibitors, which is based on molecular modeling with higher biological activity. AutoDock comparison between the new designed compound N5 and the

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template compound confirms the H-Bond between OH of sp3 hybridization of the ligand and COOH

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group of ASP25 is crucial and benefit higher biological activity.

2 Principle and methodolog 2.1 Database and biological activity

The set of 34 cyclic-urea derivatives, inhibiting HIV protease was compiled from the literature. [13] The Ki (nM) values of the HIV protease inhibitor activity data of cyclic-urea derivatives were converted into the negative logarithmic scale (log(1/Ki)). That is, pKi values were used for calculation in this paper (pKi = - logKi = log(1/Ki) ). Based on the molecules structural diverse and activities, the set was divided into the training set containing 27 compounds and the test set containing 7 compounds. The experimental activity was stemed from the literature [13], while the predicted activity was

ACCEPTED MANUSCRIPT calculated with CoMSIA model for both training and test sets of compounds. The CoMSIA model was developed using five descriptor fields: steric, electrostatic, hydrophobic, and hydrogen-bond acceptor and donor fields, the structures and activities of 34 cyclic-urea derivatives are listed in Table 1. Table 1. Structures and predicted activities of 34 cyclic-urea derivatives R/R'

Exp.(pKi) c

Pred.(pKi) d

1

CH2C6H5

8.47

8.42

2

Me

5.30

3

CH2C6H4-4-CHMe2

8.96

4*

CH2CHMe2

5.77

5

CH(Me)SMe

5.96

6

CH2-3-indolyl

6.24

7

CH2-Cy-C6H11

8

CH2CH2C6H5

9

CH2-2-naphthyl

10*

CH2-3-furanyl

8.08

8.07

11

CH2C6H4-3-SMe

8.60

8.55

12

CH2C6H4-4-SO2Me

8.60

8.59

13*

CH2C6H4-2-OMe

7.22

8.07

14

CH2C6H4-2-OH

7.46

7.42

15

CH2C6H4-3-OMe

8.33

8.32

16*

CH2C6H4-4-OMe

8.07

8.06

17

CH2C6H4-4-OH

8.96

8.97

CH2C6H4-3-NH2

8.55

8.50

CH2C6H4-3-NMe2

8.37

8.36

CH2C6H4-4-NH2

8.07

8.04

CH2-4-pyridyl

7.66

7.73

3-(2,5-Me-pyrolyl)-CH2C6H4

6.80

6.77

CH2C6H5

8.72

8.64

CH2CHMe2

7.07

7.68

25*

CHMe2

6.60

6.61

26

CH(Me)SMe

5.60

5.63

27

CH2C6H4-4-F

8.24

8.22

28

CH2C6H4-2-OMe

7.19

7.15

29*

CH2C6H4-3-OMe

9.06

8.48

30

CH2C6H4-3-OH

7.89

7.87

31

CH2C6H4-4-OMe

8.54

8.55

32*

CH2-naphthyl

8.37

8.06

33

CH2C6H3-3,5-OMe

8.57

8.58

34

CH2-2-thienyl

8.04

8.03

20 21 22 23

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5.30 8.97 5.77 5.90 6.22

7.55

7.57

6.50

6.48

8.01

7.99

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24

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19

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18

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No.

*Test set, cExperimental activity. dPredicted activity. Compounds 1-22 where (P2/P2')=benzyl, Compounds 23-34 where

ACCEPTED MANUSCRIPT (P2/P2')=CH2-Cy-C3H5

2.2 Molecular modeling and alignment

Molecular alignment is the most important part of the 3D-QSAR studies. [14] Molecular modeling

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calculations were performed by SYBYL 8.1 [15] which was optimized using the Tripos Force Field and Gasteiger-Hückel charges [16] with an energy charge of 0.001 kcal/mol, and the maximum iteration coefficient of 1000. All the 34 compounds have the same skeleton is shown in Fig. 1. Then the

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heptanuclear heterocyclic, a common substructure in the molecules database, was used as template to align the compounds set. The alignment model was used for CoMSIA studies. It is assumed that they

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have the same action mechanism and binding mode. These structures of 34 compounds were all subjected to conformational search and have minimal energy. The compound 17 was used as reference for the alignment of both the training set and test set (Fig. 2), because it is the highest biological activity for the training set. So it is also considered that the molecular structure of compound 17 is

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more stable.

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Fig. 1 Skeleton of 34 cyclic-urea derivatives

Fig. 2 The alignment of all 34 compounds 2.3 CoMSIA methodology

ACCEPTED MANUSCRIPT To generate the CoMSIA descriptor fields, a 3D cubic lattice with grid spacing of 2 Å in x, y and z directions, was created to encompass the aligned molecules. CoMSIA descriptors were calculated using the sp3 carbon probe atom with a Van der Waals radius = 1.52 Å and a charge = +1.0 to generate steric field energies and electrostatic (Coulombic potential) fields with a distance-dependent dielectric at each

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lattice point. All five force field properties (steric, electrostatic, hydrophobic, hydrogen-bond donor and hydrogen-bond acceptor) of CoMSIA were determined at a 30-kcal/mol energy cut-off, which means that energy fields greater than 30 kcal/mol are curtailed to that value, and thus can avoid infinite energy values inside the molecule. The CoMSIA method was evaluated with the help of the probe atom [17].

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Similarity indices (AF,k) are calculated at regularly spaced grid points for the pre-aligned molecular by Equation 1. −α riq2

(1)

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AFq , k ( j ) = ∑ w probe ,k wik e i

In the above equation, wprobe.k (radius =1 Å, charge =+1, hydrophobicity = +1, hydrogen bond donating = +1, hydrogen bond accepting = +1) for the probe atom was placed at each grid point to calculate the electrostatic, steric, hydrophobic, H-bond donor and acceptor fileds. The wik is the actual value of the physicochemical property k of atom i. The riq is the mutual distance between the probe atom at grid

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point q and atom i of the test molecule. The α is attenuation factor with , default value of 0.3.

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2.4 Partial least squares analysis

PLS method was used to linearly correlate the CoMSIA field to the inhibitory activity values. For PLS

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models, CoMSIA descriptors were used as independent variables and pki values were used as dependent variables [18]. The cross-validated analysis was performed using the leave-one-out (LOO) method in which one compound is removed and its activity is predicted using the model derived from the rest of the dataset. So the optimal number of components was determined by the Leave-One-Out (LOO) cross-validation procedure. A final non-cross-validation analysis was produced with the optimal number of components to obtain rcv2, which calculated by the following equation: 2. The predictive ability of the 3D-QSAR model was determined from a test set including 7 compounds (Table 1). The criteria for the 3D-QSAR model is that rcv2>0.5, rncv2>0.6. These molecules were aligned using the same method as training set, and their activities were predicted using the model generated by the

ACCEPTED MANUSCRIPT training set. Some analogues with high activity were used in the training and test set, the activity values of which were also predicted. Finally new seven compounds were designed and subsequently tested. In addition to the classical test set, these new compounds were also included in the calculation of rpred2.

rcv2

∑ (Y = 1− ∑ (Y

pred

− Yactual ) 2

actual

− Ymean ) 2

Y

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Based on the test set molecules, the rpred2 was calculated by the following equation: 3.

(2)

Y

2 rpred =

SD − PRESS SD

(3)

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Ypred, Yactual, Ymean are predicted , experimental and mean value, respectively.

SD is the sum of the squared deviations between the biological activity of the compounds in the test set

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and the mean biological activity of the training set. PRESS is the sum of squared deviation between predicted and actual activity values for each compound in test set.

2.5 Molecular docking analysis

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Molecular docking was performed by AutoDock 4.2 software package [19]. The best junction mode between the receptor and the ligand was found by using simulated annealing and genetic algorithm. Then functional form (4) [20] was used to calculate bonding free energy of the semi-empirical method

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to evaluate the binding mode between receptor and the ligand .  2  ij 

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    A Bij  qij   ∆G = ∆Gvdw ∑  12ij − 6  + ∆GH − bond ∑ E (t ) C10ij  + ∆Gele ∑ + ∆Gtor N tor + ∆Gsol ∑ (SiV j + S jVi )e r  i , j   rij  i , j ε (rij )rij i, j  r ij   ij

r

   2δ 2   

(4)

ΔGvdw, ΔGH-bond, ΔGele, ΔGtor and ΔGsol are the semi-empirical parameters obtained by fitting;

3. Results and discussion 3.1 CoMSIA analyses

Using different combinations of CoMSIA descriptor fields, different CoMSIA models were developed. In this paper, a model consisting of steric, electrostatic, hydrophobic, hydrogen-bond acceptor and hydrogen-bond donor CoMSIA fields with significant r2ncv (0.931), r2cv (0.586), r2pred (0.973) and F

ACCEPTED MANUSCRIPT (1009.45) was selected for further analysis. The contributions of steric, electrostatic, hydrophobic, hydrogen-bond acceptor, hydrogen-bond donor fields were 0.216, 0.310, 0.258, 0.085 and 0.131, respectively. So the results of CoMSIA show that steric, electrostatic and hydrophobic descriptor fields play a dominated role.

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For this CoMSIA model, it has been found that the five different descriptor fields are not totally independent to each other and the dependencies of individual fields usually affect the statistical significance of the models. All possible combinations of fields were evaluated to determine the best predictive model. With the help of a probe atom surrounding the molecules, molecular field analysis

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finds the favourable or unfavourable interaction energies of aligned molecules. These 3D colour contour maps provide hints for the modification required to design new molecules with improved

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activity.

The above results suggest the model of CoMSIA-based 3D-QSAR study is reasonable and also used to forecast the test set, to obtain the predicted activities of training set and test set compounds. The experimental and predicted pki values are displayed in Fig. 3. It can be seen that all points are located near the diagonal line and no obviously exceptional point was observed. The high rncv2, rcv2, rpred2 and F

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values indicated a good statistical correlation and reasonable predictability of the CoMSIA model. The rpred2 value of 0.973 and the results of internal and external inspection showed that the model was accurate and stable. So, the obtained QSAR model with good exterior predictive capability was robust

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and can be used to design and screen new compounds.

Fig. 3 Scatter plot between the experimental and predicted activity of 34 cyclic-urea derivatives

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(a.the steric field distribution, b. the electrostatic field distribution, c. the hydrophobic field distribution. In steric and electrostic

, respectively)

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is 80 , 20

; in hydrophobic field, that of yellow and white

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field, the distribution of green and blue is 80 , that of the yellow and red is 20

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(d. hydrogen-bond donor field distribution, e. hydrogen-bond acceptor field distribution. In hydrogen-bond donor and hydrogen-bond acceptor field, the distribution of cyan and magenta is 80 , that of blueviolet and red is 20

)

Fig. 4 CoMSIA contour maps for the highly active compound (17) of the training set

The best CoMSIA model was developed using five descriptor fields: steric, electrostatic,

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hydrophobic, hydrogen-bond donor and hydrogen-bond acceptor fields. The five descriptor fields contour maps of the highly active compound 17 are shown in Fig. 4. The green and yellow color contour maps show the favourable and unfavourable steric interactions. The blue and red contour maps

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indicate the favourable and unfavourable electrostatic interaction with the molecules. The yellow and white contour maps indicate the favourable and unfavourable hydrophobic interaction with the

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molecules, cyan and blueviolet contour maps indicate the favourable and unfavourable hydrogen-bond donor interaction with the molecules. The magenta and red contour maps indicate the favourable and unfavourable hydrogen-bond acceptor interaction with the molecules. In Fig. 4a, green contours indicate regions where steric bulk groups increase the activity, while yellow contours indicate regions where steric bulk groups decrease the activity. There is one green contour around R position, which can be explained by compound 3 and 2. Because of the different steric bulk groups it was found that the activity values of compound 3 and 2 are higher than that of 17. There is another green contour around R' position similar to the R position. In this area bulk groups increase the activity, which can be explained by compound 29 and 25. The yellow contour near P2/P2' indicate that the steric occupancy with more bulky groups in this region will decrease the activity,

ACCEPTED MANUSCRIPT which can be explained by compounds 24 and 4. For electrostatic map in Fig. 4b, blue contours indicate regions where electron positive groups increase activity, and red contours indicate regions where electron negative groups increase activity. There are blue contours near R' and P2', where the electron positive groups occupation will increase the

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activity, while there are red contours around R and P2, where the electron negative groups occupation will increase activity. It is shown that there is a large blue contour in C2- and C3- benzyl substituent for P2 or P2', where electron positive groups would increase activity.

In hydrophobic map Fig. 4c, yellow contours suggest that a hydrophobic substituent may favor

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activity, while white contours disfavor its activity. There are two white contours in the R/R' section of the benzene ring, where hydrophobic groups would decrease activity, while there is a yellow area in

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OH group of C4- benzyl substituent, where hydrophobic groups would increase activity. For H-bond donor contour maps (Fig. 4d), there are two small cyan contours around the OH groups of C4-benzyl substituent that hydrogen-bond donors in this region favor the activity, like compound 16 and 17. However, for H-bond acceptor contour maps (Fig. 4e), there are two big magenta contours around the groups on C1- and C4- heptanuclear heterocyclic substituents it shows that hydrogen-bond

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acceptors in this region favor the activity.

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3.2 Drug molecular design

Based on above CoMSIA model, the structure-activity relationships of cyclic-urea derivatives were

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obtained, that is the values of pKi have a close relationship with the fields of steric, electrostatic, hydrophobic. Compound 29 of the highest activity in the 34 cyclic-urea derivatives was selected for optimization, some compounds with higher activity based on compound 29 as template can be designed. Seven newly designed molecules and their predictive activity were shown in Table 2. Compared the seven newly designed compound with compound 29, it shows that the substitution with -OCHMe2, -OMe, -C6H5, -CHMe2 groups for compounds 1 - 4 generate better predicted activity, predicted activity of these new compounds are lower than experimental data of compond 29, but it is higher than that of the original compounds published in literature. [13] Because the increased biological activity stems from of the increased steric interaction on R/R' positions. It can also observed

ACCEPTED MANUSCRIPT that the increased electronegativity on R/P2 positions contributed to higher biological activity. It is shown that the new compound N2 possesses lower biological activity compared to the original compound 27 and 30, because the new compound N2 has lower electronegativity on R/P2 positions. Therefore, according to the information provided by CoMSIA, we can obtain higher activity drug

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molecules by changing the structure of the compounds. Table 2 Newly designed molecular structures and predictive activity R/R'

Pred.(pKi)

N1

CH2C6H4-3-OCH(CH3)2

8.551

N2

CH2C6H3-3,4-OMe

7.934

N3

CH2C6H4-3-C6H5

8.085

N4

CH2C6H4-3-CHMe2

8.542

-11.47

N5

CH2C6H4-3-CHMe2

8.963

-12.20

N6

CH2C6H4-4-OCHMe2

8.71

-11.67

N7

CH2C6H3-2,4-OH

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No.

6.956

ΔG

-10.05 -9.12

-10.59

-10.47

Compounds N5-N7 where (P2/P2')=benzyl, Compounds N1-N4 where (P2/P2')=CH2-Cy-C3H5

3.3 Docking study

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The docking studies were carried out to explore the interaction mechanism between inhibitors and the receptor. [21] The information of binding pocket of a receptor for its ligand is very important for drug design. Docking was performed using AutoDock 4.2 software package. 3D co-crystallized structure of

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HIV protease was taken from the Research Collaboratory for Structural Bioinformatics (RCSB) Protein Data Bank (PDB ID: 1AJV). [22] For the docking study, water molecules and original ligand were

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removed from the protein. The polar hydrogens and united atom Kollman charges were added for the protein so as to assign appropriate ionization states to both acidic and basic amino acid residues. In this experiment, the compound 29 template and N7 newly designed compounds were studied by molecular docking. AutoGrid was carried out for the preparation of the grid map using a grid box with a npts (number of points in xyz) = (42, 40, 40) Å box, which encloses the original ligand (NMB), and the box spacing was 0.375 Å. For compound 29 template and newly designed compound N5, grid center was designed at dimensions (x, y, z): (11.955, 21.957, 4.646) and (12.055, 22.550, 4.992), respectively. The compounds were constructed using ChemDraw 8.0 software, which were converted to 3D structures viaing energetically minimized. Then the compounds were saved as PDB file format. Lamarckian

ACCEPTED MANUSCRIPT Genetic Algorithm (LGA) was used as ligand conformation search process and the other parameters were default. The interactions of complex HIV protein-ligand conformations, including hydrogen bonds and the bond lengths were analyzed using PyMol.

As is shown in Fig. 5, for compounds N1-7 the estimated binding energy (ΔG) decreases with the

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increase of predicted biological activity, the values ofΔG were observed in Table 2. The estimated binding energy correlates with the predicted biological activity values rendering a good monotonicity,

The diagram between estimated energy of binding and biological activity

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Fig. 5

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thus demonstrating that the molecular docking calculation has certain reliability.

(a) The interactions of compound 29 and HIV protease residues

(b) The interactions of N5 and HIV protease residues

Fig. 6 The docking interaction pattern of HIV protease (1AJV) active residues with ligands It is generally believed that the small molecule drug contact with active amino acid residues of target enzyme to inhibit the activity of enzyme. Small molecules center for the active site was calculate by docking. The docked conformations showed that all compounds bind to the active residues in the

ACCEPTED MANUSCRIPT predefined hydrophobic binding pocket. As can be seen from Fig. 6 compound 29 forms hydrogen bonding (H-bond) with ASP25, and ILE50 in the binding sites. The docked compound 29 was found to have H-bond of 1.9 nm between OH of sp3 hybridization of the ligand and C=O group of ASP25 and H-bond of 2.0 nm between OH of sp3 hybridization of ligand and OH group of ASP25, as well as

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hydrogen bond of 1.8 nm between C=O of sp2 hybridization of ligand and NH2 group of ILE50. In the docked compound N5, it was found there are H-Bond of 2.0 nm between OH of sp3 hybridization of the ligand and COOH group of ASP25. Although the docked compound N5 has one H-bond, it possesses lower binding free energy comparing to compound 29. The binding free energy of

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compounds N5 and 29 are -12.20 kcal/mol, -10.63 kcal/mol, respectively. Thus, H-bond interaction between OH of sp3 hybridization of the ligand and COOH group of ASP25 played a major role in the

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combination of drugs and receptor. Due to the role of these residues, compound 29 showed a certain inhibitory activity of HIV protease. From molecular docking model, it was found that in the docking process of ligand and receptor, the formation of hydrogen bonds between the compound and receptor determined the activity of inhibitor. The positions of the active site has an important role. [23] As a common bonding between drug molecules and biological macromolecules receptor, hydrogen bonding

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makes the combination of drug molecules to the enzyme more stable and increase the drug activity. [24] The docking results agreed well with the observed biological activity datas, which showed that these

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4 Conclusions

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docking conformations were desirable to analyse the drug models.

In this paper, 3D-QSAR and molecular docking studies were performed on a series of 34 HIV protease inhibiting drug of cyclic-urea derivatives. The 3D-QSAR model was built by using CoMSIA method. The obtained model was calculated with five fields (steric, electrostatic, hydrophobic, hydrogen donor and hydrogen acceptor descriptors), which had the highest cross-validated rcv2 (0.586) and the non-cross-validated PLS analysis had a conventional rncv2 (0.931) and was considered as the best CoMSIA model. In addition, the 3D-QSAR results suggested that the steric interaction on R/R' groups and the electronegativity on R/P2 groups may enhance the inhibitory activity of enzyme. Molecular docking approach was employed to study the relationship between drug ligands and macromolecular receptor. The docking results indicated that the ligands would form hydrogen bonding interactions with

ACCEPTED MANUSCRIPT ILE50 and ASP25 of the protein receptor. More importantly, the hydrogen bonding between the OH of sp3 hybridization of the ligand and COOH group of ASP25 of the protein receptor is important for its high activity. These results demonstrated the power of a combined docking/QSAR approach to explore the probable binding conformations of compounds at the active sites of the protein target, and can also

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help to design and screen new compounds to obtain new HIV protease inhibitors with high activities.

Acknowledgements This work was supported by the National Natural Science Funds of China (21475081)(21275094), the Natural Science Foundation of Shaanxi Province of China (2015JM2057), and the

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Graduate Innovation Fund of Shaanxi University of Science and Technology.

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19. M.F. Sanners, R. Huey, S. Dallakyan, etal. Autodock4.2[CP]. The Scripps Research Institute, Jupiter FL, 2001. 20. R. Huey, G.M. Morris, A.J. Olson, D.S. Goodselle, Software news and update a semiempirical free energy force field with charge-based desolvation. J. Comput. Chem. 28(6) (2007), 1145-1152 . 21. U. Singh1, R.P. Gangwal1, G.V. Dhoke, R. Prajapati, M. Damre, A.T. Sangamwar, 3D-QSAR and molecular docking analysis of (4-piperidinyl)-piperazines as acetyl-CoA carboxylases inhibitors. Arab. J. Chem. 2012 22. K. Backbro, S. Lowgren, K. Osterlund, J. Atepo, T. Unge, J. Hulten, N.M. Bonham, W. Schaal, A. Karlen, A. Hallberg, Unexpected binding mode of a cyclic sulfamide HIV-1 protease inhibitor. J. Med. Chem. 40(6) (1997), 898-902 23. Y.T. Li, Y.L. Liu, B.Z. Shi, G.X. Wang, G.Z. Liang, Molecular docking technology for maleic imide class analysis of characteristics of the role of GSK - 3 alpha inhibitors. J. Mol. Sci. 29(4) (2013), 265-275.

ACCEPTED MANUSCRIPT 24. J.B. Tong, X. Zhao, L. Zhong, J. Chang, M.L. Li, Phenyl pyrrole class aromatase inhibitors of 3D-QSAR and

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molecular docking studies. J. Mol. Sci. 30(5) (2014), 403-409 .

ACCEPTED MANUSCRIPT 3D-QSAR and molecular docking studies on HIV protease inhibitors Jianbo Tong∗, Yingji Wu, Min Bai, Pei Zhan College of Chemistry and Chemical Engineering, Shaanxi University of Science & Technology, Xi’an 710021, PR China

Highlights

study 3D-quantitative structure activity relationship (3D-QSAR).

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·The comparative molecular similarity indices analysis (CoMSIA) technique method was obtained to

·AutoDock studies were used to find the actual conformations of chemicals in active site of HIV

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protease.

·Some new potential anti-HIV protease molecules were designed by these information.

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·Molecular docking studies were performed on some newly designed compounds.

*Correspondence author. Fax: 86-29-86168312; Tel: 86-29-86168315; E-mail address: [email protected](J.B. Tong)