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Chinese Pharmaceutical Association

Institute of Materia Medica, Chinese Academy of Medical Sciences

Acta Pharmaceutica Sinica B

Acta Pharmaceutica Sinica B 2011;1(1):27–35

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ORIGINAL ARTICLE

Pharmacophore modeling and virtual screening for the

discovery of new fatty acid amide hydrolase inhibitors$

Dong-sheng Zhaoa, Hai-yan Wangb, Zhi-hui Liana, Da-xiong Hana,n, Xin Jina

aDepartment of Pharmacy, Medical College of Xiamen University, Xiamen 361005, ChinabThird Institute of Oceanography, State Oceanic Administration of China, Xiamen 361005, China

Received 18 November 2010; revised 21 January 2011; accepted 9 March 2011

KEY WORDS

FAAH inhibitors;

Pharmacophore;

Virtual screening

stitute of Materia M

.V. All rights rese

ponsibility of Insti

11.04.003

d by the Nationalthor. Tel.: þ86 592

[email protected]

Abstract A predictive pharmacophore model has been generated from a series of diverse fatty acid

amide hydrolase (FAAH) inhibitors and the optimal pharmacophore model applied in virtual

screening. The pharmacophore model was based on a training set of 21 compounds carefully

selected from the published literatures. The optimal model Hypo-1 included four features (two

hydrogen-bond acceptor units, one aromatic hydrophobic unit and one aromatic ring unit) and two

excluded volumes. Cross-validation of the model confirmed that Hypo-1 was not generated by

chance correlation. A large test set of 55 compounds showed that Hypo-1 performed well in

classifying highly active and less active FAAH inhibitors. Superimposition analysis of the FAAH

X-ray crystal structure and the pharmacophore Hypo-1 further validated the adequacy of the

model. Virtual screening generated a total of 976 hits from the Zinc Natural Products database, a

hit rate of 1.04% and enrichment of 83.89. The acceptable hit rate further supports the use of

Hypo-1 as a 3D query tool for virtual screening.

& 2011 Institute of Materia Medica, Chinese Academy of Medical Sciences and Chinese Pharmaceutical

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edica, Chinese Academy of Medical Sciences and Chinese Pharmaceutical Association. Production and

rved.

tute of Materia Medica, Chinese Academy of Medical Sciences and Chinese Pharmaceutical Association.

Science Foundation of China (Grant No. 40976050).2188681.

n (Da-xiong Han).

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dx.doi.org/10.1016/j.apsb.2011.04.003

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dx.doi.org/10.1016/j.apsb.2011.04.003

Dong-sheng Zhao et al.28

1. Introduction

Fatty acid amide hydrolase (FAAH) is an integral membrane

protein primarily responsible for the catabolism of the fatty

acid amide (FAA) family of endogenous signaling lipids.

Substrates of FAA include the endogenous cannabinoids

(endocannabinoid), N-arachidonoylethanolamine (ananda-

mide) and 2-arachidonoylglycerol (2-AG)1. The endocannabi-

noid system is composed of two G-protein-coupled receptors,

CB1 and CB22, which are activated by signaling lipids to

modulate a range of mammalian behaviors including pain,

inflammation, appetite, motility, sleep, thermoregulation, cog-

nition and emotional state3.

FAAH inhibition leads to elevated endogenous levels of

FAAs and a range of behavioral effects. FAAH inhibition

offers an attractive way to activate the endocannabinoid

system without many side effects associated with the use of

direct cannabinoid receptor agonists4. The past 10 years have

seen an explosion in the number and quality of FAAH

inhibitors available for pharmacological evaluation, including

URB880 URB597

URB602 JP83

BMS-1 OL

OL135 Training08

Training03 Trainin

ARC70484XX

Figure 1 Molecular structures of

the compounds that have shown promising in vivo activity,

selectivity and therapeutic effects5. Kimball et al.6 introduced

a large number of potential inhibitors based on a-ketohetero-cycles (e.g. OL135, Fig. 1) that reversibly form hemiketals with

the active site serine of FAAH; Mor et al.7 introduced another

class of inhibitors derived from N-alkylcarbamates (e.g.

URB597, Fig. 1) that irreversibly acylate the active site serine

of FAAH. Other recently reported structures with inhibitory

activity against FAAH include piperidine and piperazine

ureas8, sulfonyl derivatives9, (thio)hydantoins10, boronic acid

derivatives11 and b-lactam derivatives12.

A chemical features based pharmacophore model may serve

as a guide in the screening of potential inhibitors. A pharma-

cophore represents the three-dimensional arrangement of

structural or chemical features of a drug essential for optimal

binding to the receptor13. In this report, we present the

generation and validation of a pharmacophore model of

FAAH inhibitors using the Discovery Studio 2.1/‘‘3D QSAR

Pharmacophore Generation’’ protocol. We also present the

results of virtual screening.

URB524 URB532

Training04 PF622

92 CAY10402

Training 01 Training02

g05 Training06

SPB03742 Training07

the 21 training set compounds.

Pharmacophore modeling and virtual screening for new FAAH inhibitors 29

2. Materials and methods

2.1. Molecular modeling

Molecular modeling was performed on a Silicon Graphics

work station (IBM Z-PRO 107) using Hyperchem 6.0 software

for molecular structure optimization and Discovery Studio 2.1

software (Accelrys Inc., San Diego, CA) for pharmacophore

models generation and virtual screening.

2.2. Training set and test set compound selection

Based on the published literatures, we collected 21 typical

compounds7,12,14–22 (Fig. 1) as a training set to generate

pharmacophore models of FAAH inhibitors according to a

three-dimensional quantitative structure–activity relationship.

Inhibitory activity as measured by IC50 values was in the range

0.4–5700 nmol/L spanning 5 orders of magnitude. To validate

the pharmacophore models obtained, 55 FAAH inhibi-

tors7,12,14–31 (Fig. 2) were selected to compose the test set.

For estimation purposes, activity was classified as follows:

IC50o100 nmol/L highly active (represented as þþ); 100rIC50o1000 nmol/L moderately active (represented as þ) and

IC50Z1000 nmol/L less active (represented as �).

2.3. Conformational analysis

Initially all training and test set structures were generated and

optimized using Hyperchem 6.0 software. All structures were

minimized using the Polak–Ribi�ere (conjugate-gradient) algo-

rithm with a RMS gradient set at 0.01 kcal/mol. To take into

account the molecular flexibility, the protocol ‘‘Diverse

Conformation Generation’’ in Discovery Studio 2.1 was

applied to both sets of molecules using the Poling algorithm

and selecting the ‘‘best’’ conformer generation option with an

energy threshold of 20 kcal/mol and a maximum conforma-

tion number of 300.

2.4. Generation of pharmacophores

An initial analysis of the training set using the ‘‘Create 3D

Fingerprints’’ protocol revealed that hydrogen-bond acceptor

(HBA), hydrophobic (HY) and aromatic ring (RA) features

effectively mapped the critical structural/chemical features of

all training set molecules. On this basis, we selected HBA, HY

(including hydrophobic aromatic (HY_ar), hydrophobic

aliphatic (HY_al)) and RA features for the ‘‘3D QSAR

Pharmacophore Generation’’ protocol. The minimum and

the maximum number of selected features were set at 0 and

5, respectively, the maximum number of excluded volumes

(EV) at 4, the uncertainty value at 2 (defined by software

Discovery Studio as a ratio of the reported value to the

minimum and maximum values) and the Fischer validation

at 95%.

3. Results and discussion

3.1. Pharmacophore generation

The 3D QSAR pharmacophore generation protocol generated

a set of 10 pharmacophore hypotheses from the 21 training set

compounds. All hypotheses showed high correlations indicat-

ing their stability. Three or four features (HBA, RA, HY and

HY_ar) and several excluded volumes (Table 1) were con-

sidered in all the hypotheses. The total cost of each hypothesis

is close to the fixed cost and very different to the null cost

value, indicating good hypotheses. The configuration value of

these hypotheses is 15.63, which, being less than 17, is within

the allowed range.

The optimal pharmacophore hypothesis, i.e. the one with

the highest correlation, the lowest RMS and the lowest total

cost, was named Hypo-1. It consisted of four features (HBA1,

HBA2, HY_ar and RA) and two excluded volumes (EV1 and

EV2) (Fig. 3). The RMS value of 1.47, correlation coefficient

of 0.926 and cost difference of 112.09 (Table 1) demonstrate

that there is 490% probability of accurately predicting the

activities of compounds in the training set. The reason for the

rather high value of RMS is that the uncertainty value was set

at 3 not at 2.

Table 2 shows the actual and estimated IC50 values of

training set molecules with error values, fit values and

matching degrees with the pharmacophore Hypo-1. On the

basis of the activity scale mentioned above, only one highly

active (þþ) compound was underestimated as moderate

activity (þ), one less active (�) compound was overestimated

as moderate activity (þ) and no highly active (þþ) compound

was estimated as less active (�) and vice versa. This indicates

that the pharmacophore Hypo-1 classifies the training set

compounds correctly. Fig. 3 shows the highly active com-

pound OL92 and the less active compound URB602 mapped

on Hypo-1; OL92 mapped well but URB602 missed two

features. Overall, the mapping results suggest that the phar-

macophore Hypo-1 provides a reasonable representation of

the pharmacophore of FAAH inhibitors.

3.2. Cross-validation study

The quality of the best pharmacophore hypothesis was

assessed using the Fischer validation (CatScramble test) in

Discovery Studio 2.1. This technique randomly reassigns the

activity values of the training set compounds and then creates

new spreadsheets to validate the strong correlation between

chemical structure and biological activity of training set

compounds. A total of 19 randomizations are created when

the Fischer validation is set at 95%.

The results of the Fischer validation are shown in Table 3.

The results clearly indicate that in the 19 randomizations, all

correlations are less than 0.76 and all cost values are more

than that of Hypo-1. These cross-validation results verify that

there is a 95% chance that Hypo-1 represents a true correla-

tion in the training set activity data.

3.3. Validation of Hypo-1 in the test set

The test set of 55 reported FAAH inhibitors was analyzed

to check the predictive capability of Hypo-1. The ‘‘Ligand

Pharmacophore Mapping’’ protocol with Maximum Omitted

Features set at 2 and the Fitting Method of Rigid in Discovery

Studio 2.1 was used to estimate the activities of the test set

compounds. The estimated activities and actual activities are

compared in Table 4. Of the 30 highly active (þþ) com-

pounds, none was classified as less activity (�) and only one

test01 test02 test03 test04








test33 test34 test35






Figure 2 Molecular structures of the 55 test set compounds.


Table 1 Statistical significance, predictive power and features of the top-10 pharmacophore hypotheses derived from the training

set of molecule.

Hypothesis Correlationa RMSb Total cost DCostc Featuresd EV

Hypo-1 0.926 1.47 106.63 112.09 HBA HBA RA HY_ar 2

Hypo-2 0.922 1.55 117.08 101.64 HBA HBA HY HY_ar 3

Hypo-3 0.918 1.54 106.84 111.88 HBA HBA RA HY_ar 3

Hypo-4 0.906 1.67 117.34 101.38 HBA HBA HY_ar 2


Hypo-6 0.896 1.73 118.01 100.71 HBA HBA HY RA 4



Hypo-9 0.877 1.87 120.95 99.77 HBA HBA HY 3

Hypo-10 0.865 1.95 124.4 94.32 HBA HBA HY RA 3

Fixed cost¼80.3, null cost¼218.72 and configuration¼15.63.aCorrelation coefficient based on linear regression derived from the geometric fit index.bRMS, the deviation of log (estimated activity) from log (measured activity) normalized to log (uncertainty).cDCost¼null cost�total cost.dHBA, hydrogen-bond accepter; HY, hydrophobic; HY_ar, hydrophobic_aromatic; EV, excluded volume.

Figure 3 Mapping of top scoring pharmacophore Hypo-1 on (a) highly active training set compound OL92; (b) less active training set

compound URB602; (c) highly active test set compound test 55 and (d) less active test set compound test 52. Cyan sphere, HBA; green

sphere, HY_ar; yellow/brown sphere, RA; black sphere, EV.


was underestimated as moderately active (þ); of the 16

moderately active (þ) compounds, all were predicted cor-

rectly; of the other 9 less active (�) compounds, none was

overestimated as highly active (þþ) but 7 were classified as

moderately active (þ) with most of their error values close to

1. On this basis, it can be concluded that Hypo-1 is clearly able

to correctly classify the compounds outside the training set.

Mapping of one highly active and one less active compound in

the test set on Hypo-1 is shown in Fig. 3(c) and (d).

3.4. X-ray crystallographic analysis

Mileni et al.4 presented the X-ray crystal structure of the complex

of OL135 and FAAH (PDB ID: 2WJ2) (Fig. 4). Superposition of

Hypo-1 onto FAAH reveals a good agreement of the critical

interacting chemical features. As shown in Fig. 4, OL135 in the

crystal structure and in the training set adopts the similar

conformations, the pyridine group, oxazole group and benzene

ring separately mapping onto the HY_ar, HBA1 and RA groups

correctly. In the X-ray crystal structure, the hydrophobic tail and

the phenyl group of OL135 are compressed by the hydrophobic

and aromatic Phe381, Phe432, Met436 and Met495 residues to

form a complementary cavity with a corresponding RA feature;

FAAH has an unusual Ser241–Ser217–Lys142 catalytic triad, the

carbonyl group of OL135 undergoing nucleophilic attack by the

Ser241 residue. Corresponding to Hypo-1, there is an HBA

feature indicating that potential hits must have a hydrogen-bond

accepter group at this position. The HY_ar feature of Hypo-1 is

also compressed by the hydrophobic Phe381, Leu278 and

Table 2 Actual IC50 values and their estimated IC50 of 21 training set molecules calculated on the basis of the pharmacophore

Hypo-1.

No. Name Fita Mappingb Est.c (nmol/L) Est. scale Act. scale Act.d (nmol/L) Errore

HBA1 HBA2 HY_ar RA

1 Training05 8.94 16 24 28 1� �

0.34 þþ þþ 0.4 �1.2

2 OL92 8.05 � 17 20 1� �

2.6 þþ þþ 0.44 þ5.9

3 URB880 8.75 20 6 21 12� �

0.52 þþ þþ 0.63 �1.2

4 BMS1 7.88 15 26 31 ��

3.9 þþ þþ 2 þ1.9

5 Training06 7.64 � 19 22 1� �

6.6 þþ þþ 2 þ3.3

6 URB597 7.46 25 11 � 17� �

10 þþ þþ 4.6 þ2.2

7 Training01 7.53 23 3 1 ��

8.5 þþ þþ 5 þ1.7

8 CAY10402 7.88 � 15 19 1� �

3.8 þþ þþ 9 �2.4

9 Training04 7.54 27 � 2 19� �

8.3 þþ þþ 12 �1.4

10 JP83 7.56 15 � 29 1� �

7.9 þþ þþ 14 �1.8

11 OL135 7.24 � 17 23 1� �

17 þþ þþ 15 þ1.1

12 PF622 6.9 � 26 23 4� �

36 þþ þþ 33 þ1.1

13 Training08 7.08 � 12 4 14� �

24 þþ þþ 43 �1.8

14 ARC70484xx 6.92 32 18 16 ��

35 þþ þþ 59 �1.7

15 URB524 5.62 � 4 8 ��

600 þ þþ 63 þ9.5

16 Training07 7.14 31 15 1 ��

21 þþ þþ 64 �3

17 URB532 5.62 � 15 10 ��

700 þ þ 390 þ1.8

18 Training02 5.61 � � 20 1� �

710 þ þ 400 þ1.8

19 SPB03742 6.12 � 10 12 22� �

220 þ þ 690 �3.2

20 Training03 5.65 24 5 � 17� �

660 þ � 4000 �6.1

21 URB602 5.3 � 11 13 ��

1500 � � 5700 �3.9

aFit value calculated by geometry fitting between the hypothesis and the compound; the higher the value, the better the fit.bMatching degree of pharmacophore element with the specific conformation of the compound; n means no matching; HBA, hydrogen-

bond accepter; HY, hydrophobic; HY_ar, hydrophobic_aromatic; EV, excluded volume.cEstimated IC50 value.dActual IC50 value.eError factor calculated as the ratio of the measured activity to the estimated activity or the inverse if the estimated activity is greater than

the measured activity. If measured activity is greater than the estimated activity, the error factor is negative and vice versa.

Table 3 Results from cross-validation run using CatScramble technique.

Validation Cost Correlation Validation Cost Correlation

Unscrambled 106.63 0.926 Random10 162.8 0.7

Random1 170.69 0.685 Random11 176.68 0.613

Random2 161.6 0.689 Random12 151.72 0.737

Random3 175.09 0.626 Random13 154.88 0.729

Random4 174.43 0.649 Random14 153.37 0.733

Random5 166.85 0.676 Random15 172.49 0.64

Random6 165.35 0.667 Random16 154.96 0.736

Random7 176.33 0.634 Random17 155.72 0.752

Random8 156.89 0.73 Random18 153.05 0.737

Random9 165.58 0.689 Random19 163.5 0.691


MET191 residues. In conclusion, the superposition studies

provide strong evidence that Hypo-1 is a good representation

of the pharmacophore of FAAH inhibitors.

3.5. Virtual screening

In order to further validate the discriminatory power of

Hypo-1 and to discover new potential FAAH inhibitors, 30

active FAAH inhibitors and 93,527 molecules from the Zinc

Natural Products database (2008.05)32 (to serve as decoy) were

screened. FAAH inhibitor enrichment of the database com-

pounds was expressed as enrichment factor (EF) calculated

using the following equation33:

EF¼TP=n

A=N

where TP is the number of FAAH inhibitors matched by

Hypo-1, n is the number of FAAH inhibitors and decoy

Table 4 Actual IC50 values and their estimated IC50 of 55 test set molecules calculated on the basis of the pharmacophore

Hypo-1.

Name Fita Est.b

(nmol/L)

Est.

scale

Act.

scale

Act.c

(nmol/L)

Errord Name Fita Est.b

(nmol/L)

Est.

scale

Act.

scale

Act.c

(nmol/L)

Errord

Test01 5.62 694.6 þ þ 360 þ1.93 Test29 6.82 43.99 þþ þþ 64 �1.45

Test02 6.86 39.85 þþ þþ 11 þ3.62 Test30 7.09 23.53 þþ þþ 4 þ5.88

Test03 5.62 688.81 þ � 1857 �2.7 Test31 7 28.66 þþ þþ 26.7 þ1.07

Test04 7.44 10.47 þþ þþ 49.6 �4.74 Test32 6.9 36.75 þþ þþ 10.3 þ3.57

Test05 7.36 12.54 þþ þþ 26.5 �2.11 Test33 7.37 12.3 þþ þþ 13 �1.06

Test06 7.25 16.4 þþ þþ 9.1 þ1.8 Test34 6.69 59.21 þþ þþ 23 þ2.57

Test07 5.62 694.05 � � 1700 �2.45 Test35 6.1 232.81 þ � 1167 �5.01

Test08 8.75 0.52 þþ þþ 0.63 �1.21 Test36 7.19 18.64 þþ þþ 70 �3.75

Test09 5.55 810.35 þ þ 252 þ3.22 Test37 6.6 72.99 þþ þþ 64 þ1.14

Test10 5.59 742.71 þ þ 176 þ4.22 Test38 7.56 7.94 þþ þþ 12 �1.51

Test11 5.61 704.32 þ � 1029 �1.46 Test39 7.62 6.95 þþ þþ 34 �4.9

Test12 5.62 688.04 þ � 2737 �3.98 Test40 7.15 20.71 þþ þþ 50 �2.41

Test13 5.61 705.46 þ � 3776 �5.35 Test41 5.62 687.77 þ þ 109 þ6.31

Test14 5.62 693.24 þ þ 766 �1.1 Test42 7.7 5.83 þþ þþ 7 �1.2

Test15 5.62 688.73 þ þ 696 �1.01 Test43 6.64 65.5 þþ þþ 90 �1.37

Test16 5.62 688 þ þ 113 þ6.09 Test44 6.58 75.85 þþ þþ 30 þ2.53

Test17 6.29 147.92 þ þþ 33 þ4.48 Test45 7.69 5.9 þþ þþ 20 �3.39

Test18 5.61 706.06 þ þ 160 þ4.41 Test46 5.61 707.2 þ þ 400 þ1.77


Test20 5.62 687.73 þ þ 139 þ4.95 Test48 5.87 394.02 þ þ 315 þ1.25

Test21 7.17 19.73 þþ þþ 5.3 þ3.72 Test49 5.62 688.3 þ � 2100 �3.05

Test22 6.6 73.57 þþ þþ 13 þ5.66 Test50 5.62 687.68 þ þ 590 þ1.16

Test23 6.82 43.4 þþ þþ 25 þ1.74 Test51 5.46 991.4 þ � 1200 �1.21

Test24 5.61 710.87 þ þ 174 þ4.09 Test52 5.38 1193.39 � � 5700 �4.78


Test26 5.62 687.7 þ þ 808 �1.17 Test54 7.68 6.01 þþ þþ 30 �4.99

Test27 7.91 3.55 þþ þþ 10 �2.82 Test55 8.81 0.45 þþ þþ 0.4 þ1.12

Test28 7.88 3.82 þþ þþ 13 �3.4

aFit value calculated by geometry fitting between the hypothesis and compound; the higher the value, the better the fit.bEstimated IC50 value.cActual IC50 value.dError factor calculated as the ratio of measured activity to estimated activity or the inverse if the estimated activity is greater than the

measured activity. If measured activity is greater than estimated activity, the error factor is negative and vice versa.

Trp531

Gly485

Val491Ser217

Ser241Lys142

Met191Leu154

Gly268Leu278

Phe381

Ile238

Phe192

Phe381

Met436

Met495Phe432

Figure 4 Superposition of the pharmacophore Hypo-1 with the FAAH crystal structure. The molecule in red is OL135 in the crystal

structure and the molecule in blue is OL135 in the training set.


Figure 5 Mapping of the hit compounds (a) ZINC06623865 and (b) ZINC02260731 from the Zinc Natural Products database onto the

pharmacophore Hypo-1. Cyan sphere, HBA; green sphere, HY_ar; yellow/brown sphere, RA; black sphere, EV.


molecules matched by the model, A is the number of known

FAAH inhibitors in the decoy and N is the number of

compounds in the decoy. As before flexibility was taken into

account by applying the ‘‘Diverse Conformation Generation’’

protocol in Discovery Studio 2.1. This generates diverse

conformers for all the compounds in the database using the

Poling algorithm by first selecting the best conformer genera-

tion option with an energy threshold of 20 kcal/mol and a

maximum conformation number of 300 and then using the

‘‘Ligand Pharmacophore Mapping’’ protocol with the same

parameters as in the test set analysis. In this way 27 of 30

FAAH inhibitors (90%) and 976 compounds in the Zinc

Natural Products database ( hit rate of 1.04%) matched

Hypo-1 model with estimated IC50 valueso100 nmol/L.

Taking this into account, an EF of 83.89 was calculated

for Hypo-1, showing it is highly restrictive in picking hits

from a chemistry database. The high performance of this

virtual screening further proves that Hypo-1 is very useful as

a 3D query tool in the screening of other databases.

Two compounds from the Zinc Natural Products database

predicted to be highly active (ZINC06623865 and

ZINC02260731) based on mapping onto Hypo-1 are shown

in Fig. 5.

4. Conclusions

This paper reports the use of ligand-based pharmacophore

generation and virtual screening methods to identify the ligand

requirements of FAAH inhibitors. The study presents the 3D

QSAR pharmacophore models for FAAH inhibitors and

establishes that the best pharmacophore Hypo-1 contains four

features (two hydrogen-bond acceptors, one aromatic hydro-

phobic group and one aromatic ring group) and two excluded

volumes. The pharmacophore model provides high predict-

ability for estimating the activities of a variety of compounds.

The virtual screening results provide a total of 976 new hits

from 93,527 molecules, proving the model is highly restrictive

in picking up the novel hits from the chemical databases. To

progress the discovery of novel FAAH inhibitors, some of the

hit compounds are currently synthesized and tested for activity

determined. In addition, the pharmacophore Hypo-1 mapping

approach is shown to be useful in understanding the impor-

tance of various chemical features in FAAH inhibitors, in

further virtual screening and in the design and optimization of

a new generation of FAAH inhibitors.

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