Pharmacophore Screening on Piperidinecarboxamides Derivatives Based on GALAHAD and CoMFA Models

FULL PAPER

* E-mail: [email protected] to [email protected] Received October 27, 2010; revised January 5, 2011; accepted February 25, 2011. Project supported by the National Natural Science Foundation of China (No. 20872095).

Chin. J. Chem. 2011, 29, 1075—1083 © 2011 SIOC, CAS, Shanghai, & WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim 1075

Pharmacophore Screening on Piperidinecarboxamides Derivatives Based on GALAHAD and CoMFA Models

Liu, Minga(刘明) Wu, Qiangsanb(吴强三) Hu, Wenxiang*,a,b(胡文祥) a College of Life Science, Capital Normal University, Beijing 100048, China

b Department of Chemistry, Capital Normal University, Beijing 100048, China

In order to discover the novel anticonvulsant drugs, pharmacophore screening of the anticonvulsant inhibitors was enforced. Genetic Algorithm with Linear Assignment for Hypermolecular Alignment of Datasets (GALAHAD) and Comparative Molecular Field Analysis (CoMFA) studies were combined to implement our research. Firstly, multiple models were generated using GALAHAG based on high active molecules. Secondly, several of them were validated using the CoMFA study. Finally, a good values of q2 from training set and promising predictive power from test set were obtained based on one model simutaneously. One model had been selected as the most reasonable pharmacophore model. The results of the CoMFA study based on the model 1 suggested that both steric and elec-trostatic interactions played important roles.

Keywords pharmacophore, comparative molecular field analysis (CoMFA), genetic algorithm with linear assignment for hypermolecular alignment of datasets (GALAHAD), piperidinecarboxamide

Introduction

Epilapsy is a major neurological disorder throughout the world.1,2 Several reports have estimated that ap-proximately 25%—30% of epileptic patients do not re-spond to current therapeutic treatments.3 Therefore, new and less toxic, as well as more effective drugs are re-quired.4 Although some new drugs such as igabatrin, lamotrugine, gabapentin, tiagabine, felbamate and topira have appeared on the market,5 the development of novel agents, particularly compounds effectively against com-plex partial seizures remain as a major focus of antiepi-leptic drug research.6 Since the 2-piperidinecarboxamide and its derivatives represent a novel series of anticonvul-sant compounds, which are active in the maximal elec-troshock seizure (MES) test in mice,7-10 further explora-tion of this type of compounds should be noteworthy.

In our study, we have performed pharmacophore screening by Genetic Algorithm with Linear Assign-ment for Hypermolecular Alignment of Datasets (GALAHAD) alignment11-13 and Comparative Molecu-lar Field Analysis (CoMFA) study,14-16 which could serve as a useful tool to gain pharmacophore model of anticonvulsant inhibition and predict the inhibitory properties of newly designed compounds.

The alignment of ligands can be performed using the methods based on the atom root meam square (RMS) fitting, pharmacophore or feature alignments.17-19 Stud-ies indicate that the alignment approaches based on fea-tures (e.g. GASP, DISCO, FlexS and GALAHAD) are more advantageous than the scaffold superposition util-izing the atom RMS fitting in aligning flexible and di-

verse compounds.20-24 Aligning a set of flexible and biologically active ligands in three dimensions is the key to pharmacophore generation.15,16 Since the active con-formation of molecules is usually unknown, the fore-most difficulty in pharmacophore generation is the han-dling of conformational flexibility, the DISCO program operates it in a limited conformational space, whereas GASP operates it by torsioning sampling of the full range of conformations that are energetically accessible. The performance of these programs was recently criti-cally compared with respect to each other and the cor-responding functions in Catalyst.25 DISCO and Catalyst ignore steric overlap altogether, strain energy is incor-porated directly into the GASP fitness function. It is dealt with indirectly in DISCO and GASP by filtering out high-energy conformers during pre-processing. Al-though each of the three programs has strengths, unfortu-nately, the optimal configurations with respect to each criterion rarely coincide.

GALAHAD is a novel pharmacophore screening module, it employs Tripos’ proprietary technology to generate pharmacophore hypothesis and alignments from sets of ligand molecules that bind at a common target site. The process is divided into two steps. First, the ligands are aligned to each other in internal coordi-nate space, in this stage a genetic algorithm (GA)26 was used to identify a set of ligand conformations that minimizes energy while maximizes pharmacosteric similarity. At the same time pharmacophore multiplet similarity between ligands is maximized, this stage is fully flexible. The second step is a rigid-body hypermo-

Liu, Wu & HuFULL PAPER

1076 www.cjc.wiley-vch.de © 2011 SIOC, CAS, Shanghai, & WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim Chin. J. Chem. 2011, 29, 1075—1083

lecular alignment process in Cartesian space. This pro-gram is an extension of the earlier program that worked from atomic properties.27 Similar to its earlier applica-tion, it makes use of linear assignment methodology and geometric heuristics to identify optimal mapping. Hy-permolecular construction program makes use of linear assignment to identify optimal feature correspondences between ligands (LAMDA: Linear Assignment for Mo-lecular Database Alignment). The program identifies corresponding features in ligands paired on the basis of structural similarity and filters those correspondences based on geometric consistency. Correspondences that pass these filters are used to do a least-squares align-ment of the two ligands, and they are merged into a sin-gle hypermolecule. The best models are carried forward into the second stage, multiplemodels are produced be-cause a multi-objective fitness function is used in the GA, and every model represents a different trade-off among the competing criteria,28 but rarely do models achieve optimum values in all parameters. In order to evaluate the performance of the GALAHAD models and select a most reasonable model as the pharmacophore, the 3D-QSAR based on several models being generated by GALAHAD have been performed.29,30

The 3D-QSAR has been traditionally perceived as a means of establishing correlations between trends in chemical structure modifications and respective changes of biological activity.31 Once the QSAR model valida-tion32 has been achieved, the predictive power of the model and its ability to reproduce biological activities of untested compounds is established. Comparative Mo-lecular Field Analysis (CoMFA) remains as the most popular QSAR method mainly because the graphic re-sults of CoMFA models can provide a direct way to visualize the structure-activity relationship.33 So 3D-QSAR study used the CoMFA technique in our study. The outcome of this procedure is cross-validated. Studies of q2 have indicated that while high value of q2 is the necessary condition for a model to have a high predictive power, it is not a sufficient one.34 In order to validate the templates, one needs to split the whole available dataset into the training and test set. The model which has high value of the training set q2 and the high predictive power of the test set have been se-lected as the pharmacophore model.

Therefore the most reasonable pharmacophore model was selected by using the methods that GALAHAD and 3D-QSAR were combined. Then the pharmacophore model might be used in database searching. We believed that this procedure would be helpful in the discovery of novel anticonvulsant inhibitory compounds.

Results and discussion

Pharmacophore hypothesis generation

The LAMDA algorithm was applied through the GALAHAD interface released with SYBYL7.2. Once GALAHAD alignment based on 10 high active com-

pounds was completed, a total of twenty pharmacophore models were generated using common feature hypothe-sis generation approach. According to the two principals noted previously, models 1, 2, 6 and model 20 are se-lected listed in Figure 1. They all had an acceptable balance between energy, pharmacophoric coherence and pharmacosteric overlap statistically. Meanwhile low energy conformers of the high active compounds (1, 2, 11, 21) mapped to them perfectly. As shown in Figure 1, the generated pharmacophore models contained two hydrophobic regions, one hydrogen bond acceptors, one hydrogen bond donor and one protonated N group. They were featured as the blue ball, the green ball, the purple ball and the red ball respectively. The sphere sizes indi-cated query tolerances.

Figure 1 Four models selected out of 20 models generated by GALAHAD.

Piperidyl group, amide group and phenyl group were all included in 10 high active compounds features (listed in Table 1). As we known, phenyl is the hydrophobic group, the oxygen atom in amide group is an elec-tron-rich atom. Once aligning process was completed based on these similar features, each cluster whose population exceeded the minimum number of molecules Tmin (the minimum number of ligands’ features must ‘‘hit’’ to be included in the query) was required to “hit”. The representative feature, hydrophobic region based on phenyl and one hydrogen bond donor based on the oxy-gen atom in amide group, were created correspondingly. Other features in pharmacophore models were generated in the same way. However, when the minimum number of molecules Tmin can not get to the “hit”, the represen-tative features could not be created, such as model 5 listed in Figure 2. Although several nitrogen atoms in amide group aligned in the same region, the molecular number that aligned in this region failed to get the “hit” number which was five signed by us and thus in this similar region compared model 1 also listed in Figure 2, the donor and protonated N features of model 5 failed to create.

Pharmacophore Screening on Piperidinecarboxamides Derivatives

Chin. J. Chem. 2011, 29, 1075—1083 © 2011 SIOC, CAS, Shanghai, & WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim www.cjc.wiley-vch.de 1077

Figure 2 Model 1 and model 5 generated by GALAHAD based on 10 high active compounds.

Statistical analysis of CoMFA models

In order to select the most reasonable pharma-cophore model, the CoMFA studies were performed based on four pharmocophore hypothesis as shown in Figure 1 respectively. The results of the PLS analysis are listed in Table 1.

Table 1 The best CoMFA models obtained from the alignment based on four models respectivelya

Model q2 n r2 F S Ste Ele

Model 1 0.571 2 0.999 2426.414 0.007 0.471 0.529

Model 2 0.218 2 0.995 585.269 0.016 0.389 0.611

Model 6 0.226 6 0.994 54.194 0.020 0.406 0.594

Model 20 0.508 3 0.856 26.358 0.205 0.436 0.568 a q2 is cross-validation correlation coefficient, n is optimum prin-ciple number of components, r2 is correlation coefficient, S is standard error of prediction, F is standard error of predictions. Ste is the steric contribution, Ele is the electrostatic contribution.

Analysis of the statistical evaluation values for all models in Table 2 consistently showed that the contri-bution of the electrostatic features to activity is greater than that of steric effects. The results listed in Table 2 showed that model 2 and model 6 had lower q2. The values of q2 from model 2 is 0.218 and the values of q2 is 0.226 from model 6. Lower q2 indicated the lower predictive ability. So they could not be served as the pharmacophore models. Model 1 and model 20 both have the relatively high q2 value. It is widely accepted that a correlation with a q2 value greater than 0.5—0.6 is useful for the prediction of new biologically active molecules. Therefore it can be concluded that only model 1 and model 20 have the chance of performing the high predictive power. Their predictive ability would be further validated by the prediction for the external test set. The predicted values of 4 compounds in the test set as well as their experimental values and residual values are given in Tables 2 and 3.

The predictions are close to the experimental values with a maximum deviation of 0.382 in the log scale based on model 20, while the values is 0.135 based on model 1 and the results of this procedure indicate that model 1 has the higher predictive power than model 20. Meanwhile the values of S and the q2 derived from model 1 are both better than that from model 20. Overall

Table 2 The observed versus predicted activity and residual for the test set based on the model 1

Compound Exp-value Pre-value Residual

18 1.79 1.701 0.079

24 1.58 1.617 －0.037

25 1.49 1.475 0.015

30 2.00 1.861 0.135

Table 3 The observed versus predicted activity and residues for the test set based on model 20


18 1.79 1.462 0.328

24 1.58 1.745 －0.174

25 1.49 1.671 －0.181

30 2.00 1.618 －0.328

the results indicated that model 1 possessed a promising predictive ability for the compounds randomly selected. The final predicted/cross-validated versus experimental －lg(ED50) values for model 1 and their residuals are given in Table 4.

Table 4 The observed versus predicted activity and residues for the training set based on model 1


4 1.62 1.622 0.001

5 1.69 1.685 0.005

7 1.45 1.452 －0.005

12 1.48 1.481 －0.004

13 1.77 1.771 0.000114

14 2.26 2.248 0.007

15 1.88 1.894 －0.013

16 1.54 1.538 0.006

17 1.74 1.732 0.008

19 1.93 1.924 0.006

20 1.63 1.625 0.005

22 1.72 1.736 －0.012

23 1.46 1.458 0.004

27 2.00 2.002 －0.007

28 1.85 1.848 －0.003

29 1.58 1.579 0.001

31 1.57 1.567 0.001

32 1.89 1.885 0.001

33 1.742 1.739 0.003

34 1.58 1.581 0.001

The graphs of actual versus fitted and predicted ac-

tivities for the training and test set of molecules based on model 1 are depicted in Figure 3 respectively. As can be seen, there was no obvious warp from the linearity regions, which indicated that model 1 had the promising



predictive power.

Figure 3 Predicted versus observed values derived from the CoMFA model 1 of the training and test set.

The low energy conformer of high active compounds (compounds 1, 2, 11 and 21) all mapped the model 1 perfectly (Figure 4). It indicated that model 1 was most close to the real pharmacophore. Hence model 1 was selected as the pharmacophore model reasonably.

Figure 4 Low energy conformers of the high active compounds (1, 2, 11, 21) mapped to model 1 perfectly.

Visual inspection of 3D courtour maps generated by CoMFA

CoMFA contour maps were generated to visualize the information content of the 3D-QSAR models. Our study was made on the pharmacophore model 1. It showed significance of the electrostatic and steric en-ergy values for biological potency. These contour maps could provide a graphic guidance to the design of higher bioactive molecules.

Analysis of steric field maps In Figure 5, the steric contour plot of pharmacophore model 1 was dis-played. The green contour indicated the regions where an increase in steric bulk would enhance the anticon-vulsant activity; while the yellow contour denotes the areas where a decrease in steric bulk would enhance the anticonvulsant activity. For instance, the large green contour situated near the protonated N suggested that the additional bulky groups in this region will increase the anticonvulsant activity.35 As we expected, with re-

gard to four compounds (compound 21, 1, 11, 12), the bulkiness of the group situated near protonated N is di-minished, their activity was diminished according to the corresponding order.

Figure 5 Contour maps for model 1 with the pharmacophore model 1 inside the steric field.

Analysis of electrostatic field maps The electro-static contour maps of pharmacophore model 1 are dis-played in Figure 6. The red polyhedral displays regions where partial negative charge favors the bioactivity; while the blue polyhedral displays regions where partial negative charge disfavors the activity. As seen, the hy-drophobic feature in the right regions in Figure 6 is sur-rounded by the blue polyhedral. Electronegative frag-ments at this position may decrease the activity which may be the reason why the activities of compounds 1 and 2 are higher than those of compounds 6 and 7.

Figure 6 Contour maps for model 1 with the pharmacophore model 1 inside the electrostatic field.

Conclusions

The pharmacohore screening using GALAHAD module had been performed based on high active anti-convulsant molecules. The generated pharmacophore models contained 2 hydrophobic centers, one protonated N region, one hydrogen bond acceptor region and a hy-drogen bond donor. In order to select the most reason-able model to serve as the anticonvulsant pharma-cophore model, the 3D-QSAR analysis using CoMFA method based on pharmacophore had been successfully applied to the training set and an external set. Using this method, model 1 had been selected as the most reason-able pharmacophore model.



The results of the CoMFA study based on model 1 suggested that both steric and electrostatic interactions play important roles. The contributions from steric and electrostatic fields for model 1 were 0.471 and 0.529 respectively, which indicates that electrostatic interac-tions of the molecules with a biological receptor con-tribute to biological activities to a greater extent than steric effects. The CoMFA contour maps provided a graphic guidance to the design of higher bioactive molecules. Virtual screening of the commercial database is going to be made in the future by using a three di-mensional pharmacophore generated from our studies .

Experimental section

Materials

Thirty-four compounds which had anticonvulsant ac-tivity listed in Table 5 (compounds 1—21 were obtained

from reference 36 and compounds 22—34 were obtained from refference 37) were collected from the literature.36,37 These compounds were 2-piperidinecarboxamide and its derivatives. The anticonvulsant activity of the target compounds in our investigation was evaluated in the maximal electroshock seizure (MES) test in mice by the National Institute of Neurological Disorders and Stroke (NINDS). ED50 values were converted into －lg ED50 values listed in Table 5 and used as the dependent col-umn. The 10 most active compounds were selected from Table 5 marked with an asterisk (*) as the data set to carry out the GALAHAD study. Otherwise, the next 24 compounds were separated into a training set with 20 molecules and a test set with 4 molecules marked with symbol # in order to test the prediction of the derived CoMFA models. These studies were carried out on a Redhat Linux WS 3.0 computer using the SYBYL 7.2 software package installed on a Pentium 3.6 GHz PC.

Table 5 The structure and anticonvulsant activities of 34 moleculesa

Compound R1 R2

n R3 Exp-values －lg ED50

1*

0 0.76

2*

0 0.92

3*

1 H 1.38

4

1 H 1.62

5

1 CH3 1.69

6*

1 CH3 1.30

7

1 CH3 1.45

8*

1 CH(CH3)2 1.34



Continued

Compound R1 R2


9*

1

1.34

10*

0 0.99

11*

0 0.76

12 NH2

0 1.48

13

1 CH3 1.77

14

1 CH3 2.26

15

1 CH3 1.88

16

0 1.54

17

0 1.74

18#

0 1.79

19

0 1.93

20

1 CH3 1.63

21*

0 0.41

22

1 H 1.72



Continued

Compound R1 R2


23

1 H 1.46

24#

1 H 1.58

25#

1 H 1.49

26*

1 H 1.38

27

1 H 2.00

28

1 H 1.85

29

1 H 1.58

30#

1 H 2.00

31

1 H 1.57

32

0 1.89

33

0 1.74

34

1 CH3 1.58

a Indicates the combined position, when n＝0, group R2 was bonded to amide nitrogen.

Method

GALAHAD alignments All 3D structures were generated using CONCORD module.38 They were as-signed with Gasteiger-Hückel charges39,40 and mini-mized with Tripos force field41 subsequently. The gra-dient was at 0.021 kJ•A•mol－1, the Max iterations was at 10000 unless otherwise noted. Defaulted configura-

tions and parameters were used. The 10 most active compounds marked with asterisk (*) in Table 5 were used to perform the pharmacophore hypothesis using the GALAHAG module in Sybyl 7.2.

Once the GALAHAD alignment was completed, a series of models was produced because a multi-objective fitness function was used in the GA. A set of diverse



models allow the users to choose the model that corre-sponds to the most appropriate underlying parameter weights. But if only one model was selected, the risk of the selection was existent. In order to lower the risk, four similar models listed in Figure 1 were chosen based on the following two criteria for further study. The se-lected models must have an acceptable balance between energy, pharmacophoric coherence and pharmacosteric overlap. High active compounds (compounds 1, 2, 11, 21) should be all ideally fit in the pharmacophore models.

CoMFA study In order to evaluate the performance of the GALAHAD models, a CoMFA study was per-formed using steric and electrostatic fields in Sybyl 7.2. Four models were used as templates in structure align-ment for 20 molecules of the training set respectively. In this procedure, GALAHAD module was used to align the training set molecules to the selected templates re-spectively. The CoMFA fields were generated using a sp3 carbon atom with a ＋1 charge as the probe. The default grid spacing (2 Å) was employed, which ex-tended 5.0 Å units in all directions beyond the dimen-sions of each molecule. Energy cutoff values of 28 kcal/mol were selected for both the electrostatic and steric fields. The Partial Least Squares (PLS) regres-sion42,43 was used to linearly correlate the CoMFA fields to biological activity values. The cross-validated analy-sis was performed using the leave-one-out (LOO) method that was utilized to optimize the number of principal components and to evaluate the predictive power of models first. Then non-cross-validated analy-sis was performed. Column filtering for the cross- validated and non-cross-validated CoMFA analysis was set to 8.36 and 0.418 kJ/mol respectively. The optimal number of components was obtained from the LOO analysis that cross-validated r2 was maximal and the standard error of prediction was minimal.

The advanced validation of model predictivity The results of the PLS analysis are listed in Table 1. The low values of q2 for the training set can indeed serve as an indicator of a low predictive ability of a model. High values of q2 can be only regarded as a necessary condi-tion, but not a sufficient one for a model to possess sig-nificant predictive power. According to the values of the q2, model 1 and model 20 were chosen for further study. Knowing the risk of utilizing the leave-one-out q2 as a criterion for selecting the best model,32,44 a further test on the CoMFA models was validated by an external test set of 4 compounds to validate the predictive power of model 1 and model 20. The results are listed in Tables 2 and 3 respectively. After obtaining the models, the CoMFA results were graphically interpreted by field contribution maps.

References

1 Cai, Q.; He, G.; Ma, D. J. Org. Chem. 2006, 71, 5268. 2 Loscher, W.; Schmidit, D. Epilepsy Res. 1994, 17, 95. 3 Mattson, R. H. Epilepia 1996, 37, S1.

4 Patsalos, P. N. Emerging Drugs 1999, 4, 87. 5 Wolfe, J. F.; Greenwood, T. D.; Mulheron, J. M. Exp. Opin.

Ther. Pat. 1998, 8, 361. 6 Ouali, A.; Spindler, J. F.; Cristau, H. J.; Taillefer, M. Adv.

Synth. Catal. 2006, 348, 499. 7 Crider, A. M.; Wood, J. D.; Tschappat, K. D.; Hinko, C. N.

J. Pharm. Sci. 1984, 73, 1612. 8 Crider, A. M.; Wood, J. D.; Tschappat, K. D.; Hinko, C. N.

J. Pharm. Sci. 1982, 71, 1214. 9 Hinko, C. N.; Scibert, K.; Crider, A. M. Neuropharmacol-

ogy 1984, 23, 1009. 10 Hinko, C. N.; Scibert, K.; Crider, A. M. Neuropharmacol-

ogy 1988, 27, 475. 11 Chen, Y.; Wang, Y.; Sun, Z.; Ma, D. Org. Lett. 2008, 10,

625. 12 Zou, B.; Yuan, Q.; Ma, D. Angew. Chem., Int. Ed. 2007, 46,

2598. 13 Nicola, J. R.; Charlene, A. A.; Philippa, R. N.; Wolohan;

Edmond, A.; Peter, W.; Robert, D. C. J. Comput.-Aided Mol. Des. 2006, 20, 567.

14 Cramer, R. D.; Patterson, D. E.; Bunce, J. D. Prog. Clin. Biol. Res. 1989, 291, 161.

15 Yip, S. F.; Cheung, H. Y.; Zhou, Z.; Kwong, F. Y. Org. Lett. 2007, 9, 3469.

16 Lemmen, C.; Lengauer, T. J. Comput-Aided Mol. Des. 2000, 14, 215.

17 Bostrom, J.; Bohm, M.; Gundertofte, K.; Klebe, G. J. Chem. Inf. Comput. Sci. 2003, 43, 1020.

18 Yuan, H.; Kozikowski, A. P.; Petukhov, P. A. J. Med. Chem. 2004, 47, 6137.

19 Li, B.; Liue, M.; Hu, W. X. Acta Phys.-Chim. Sin. 2010, 26, 206.

20 Jones, G.; Willett, P.; Glen, R. C. J. Comput.-Aided Mol. Des. 1995, 9, 532.

21 Lemmen, C.; Lengauer, T.; Klebe, G. J. Med. Chem. 1998, 41, 4502.

22 Martin, Y. C.; Bures, M. G.; Danaher, E. A.; DeLazze, J.; Lico, I.; Pavlik, P. A. J. Comput.-Aided Mol. Des. 1993, 7, 83.

23 Wellsow, J.; Machulla, H. J.; Kovar, K. A. Quant. Struct. Act. Relat. 2002, 21, 577.

24 Krovat, E. M.; Langer, T. J. Med. Chem. 2003, 46, 716. 25 Patel, Y.; Gillet, V.; Bravi, G.; Leach, A. R. J. Comput.-

Aided Mol. Des. 2002, 16, 653. 26 Holland, J. H. Sci. Am. 1992, 267, 66. 27 Richmond, N. J.; Willett, P.; Clark, R. D. J. Mol. Graph.

Model. 2004, 23, 199. 28 Cottrell, S. J.; Gillet, V. J.; Taylor, R.; Wilton, D. J. J.

Comput.-Aided Mol. Des. 2002, 18, 665. 29 Zhu, H. W.; Fang, H.; Wang, L. Y.; Hu, W. X.; Xu, W. F.

Drug Discovery Ther. 2008, 2, 192. 30 Liu, X. L.; Wang, L. Y.; Hu, W. X. Chemistry 2009, 72, 133

(in Chinese). 31 Zhang, Z. Y.; An, L. Y.; Hu, W. X.; Xiang, Y. H. J. Comput.-

Aided Mol. Des. 2007, 21, 145. 32 Golbraikh, A.; Tropsha, A. J. Mol. Graphics Mod. 2002, 20,

269. 33 Alexander, G.; Alexander, T. J. Comput.-Aided Mol. Des.



2002, 20, 269. 34 Jiang, D.; Fu, H.; Jiang, Y.; Zhao, Y. J. Org. Chem. 2007,

72, 672. 35 Smits, R. A.; Lim, H. D.; Stegink, B.; Bakker, R. A.; Esch, I.

J.; Leurs, R. J. Med. Chem. 2006, 49, 4512. 36 Bin, H.; Michael, C. A. Eur. J. Med. Chem. 2001, 36, 265. 37 Bin, H.; Prabha, M. V. Eur. J. Med. Chem. 1998, 33, 23. 38 Cristau, H. J.; Cellier, P. P.; Hamada, S.; Spindler, J. F.;

Taillefer, M. Eur. J. Org. Chem. 2004, 22, 695. 39 Gasteiger, J.; Marsili, M. Tetrahedron 1980, 36, 3219.

40 Hinze, J.; Jaffe, H. H. J. Am. Chem. Soc. 1962, 84, 540. 41 Clark, M.; Cramer, R. D.; Vanopdenbosch, N. J. Comput.

Chem. 1989, 10, 982. 42 Dunn, W. J.; Wold, S.; Edlund, U.; Hellberg, S.; Gasteiger,

J. Quant. Struct.-Act. Relat. 1984, 3, 131. 43 Cramer, R. D.; Bunce, J. D. Quant. Struct.-Act. Relat. 1988,

7, 18. 44 Boehm, M.; Stuerzebecher, J.; Klebe, G. J. Med. Chem.

1999, 42, 458.

(E1010271 Zhao, C.; Zheng, G.)

Pharmacophore Screening on Piperidinecarboxamides Derivatives Based on GALAHAD and CoMFA Models

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