Investigation into Potent Inflammation Inhibitors from Traditional Chinese Medicine

Investigation into Potent Inflammation Inhibitorsfrom Traditional Chinese Medicine

Kuan-Chung Chen1,†, Mao-Feng Sun1,2,†,Shun-Chieh Yang1, Su-Sen Chang1,Hsin-Yi Chen3, Fuu-Jen Tsai3,4 andCalvin Yu-Chian Chen1,3,5,6,*

1Laboratory of Computational and Systems Biology, School ofChinese Medicine, China Medical University, Taichung 40402,Taiwan2Department of Acupuncture, China Medical University Hospital,Taichung, Taiwan3Department of Bioinformatics, Asia University, Taichung 41354,Taiwan4Department of Medical Genetics, China Medical University,Taichung 40402, Taiwan5Department of Systems Biology, Harvard Medical School, Boston,MA 02115, USA6China Medical University Beigan Hospital, Yunlin 65152, Taiwan*Corresponding author: Calvin Yu-Chian Chen, [email protected];[email protected]�Equal contribution.

Microsomal prostaglandin E synthase-1 (mPGES-1)is the key enzyme for prostaglandin E2 (PGE2)generation during inflammation and is a potentialtarget for designing anti-inflammatory drugs.Potential inhibitors of m-PGES-1 were selectedfrom traditional Chinese medicine (TCM Data-base@Taiwan) based on the pharmacophore mapgenerated by the top HypoGen hypothesis and val-idated using structure- and ligand-based analysis.Key features for potential m-PGES-1 inhibitorsinclude pi-interactions and H-bond donors. TCMcompounds, shanciol B, shanciol A, castilliferol,and aurantiamide acetate, contoured to the quan-titative structure–activity relationship pharmaco-phore and exhibited high docking scores andbinding stability with m-PGES-1. Bioactivity mod-els multiple linear regression (MLR) and supportvector machine also supported activity predictionsfor the candidate compounds. Our results indicatethat the investigated TCM compounds could be ofuse for development into mPGES-1 inhibitors.

Key words: anti-inflammation, docking, molecular dynamics, m-PGES-1,quantitative structure–activity relationship, traditional Chinese medicine

Received 10 January 2011, revised 18 June 2011 and accepted for pub-lication 10 July 2011

Inflammations are related to many diseases, including atherosclero-sis, allergies, and cancer (1,2). In the past, the chemopreventiveeffects of nonsteroidal anti-inflammatory drugs were designed totarget cyclooxygenase-1 (COX-1) and cyclooxygenase-2 (COX-2),enzymes responsible for the biosynthesis of prostaglandins (3).However, long-term pharmacological suppression of prostanoid bio-synthesis by these drugs has severe side effects such as gastroin-testinal injury and renal irritations (4). Microsomal prostaglandin Esynthase-1 (mPGES-1) and COX-2 are the key enzymes for prosta-glandin E2 (PGE2) generation during inflammation. Hence, targetedsuppression of mPGES-1 activity can minimize these side effects,making the protein a potential drug target related to inflammation.

Computational approaches, including pharmacophore mapping,compound screening (5), docking (6), quantitative structure–activityrelationship (QSAR) (7,8), and molecular dynamics (MD) simulation(9–11), can provide timely information and insights for drug devel-opment. These methods have been comprehensively reviewed (12–14). Computational drug design efforts on mPGES-1 have beenmade in this laboratory since 2009. Critical features of mPGES-1inhibitors were identified through pharmacophore mapping (15) andused to screen for candidates from the TCM Database@Taiwan(16). Ligand de novo evolution was studied and supported by com-parative molecular field analysis (CoMFA) and comparative molecu-lar similarity indices analysis (CoMSIA) modeling (17). Moleculardynamics simulations were also employed to determine stabilityconformations of the obtained receptor–ligand complexes (18).

Natural compounds from traditional Chinese medicine (TCM) havebeen investigated as potential drugs for different diseases (19–25),and some may have anti-inflammatory functions. The present studyinvestigated potential mPGES-1 inhibitors from TCM, in hopes ofproviding useful information for drug development.

Materials and Methods

The experimental design of this study is illustrated in Figure 1.

Generation of pharmacophore hypotheses withHypoGenPharmacophore hypotheses (Catalyst HypoGen) were performed bythe 3D Quantitative Structure-Activity Relationship (QSAR) Pharmaco-phore Generation protocol of Discovery Studio 2.5 (DS 2.5; Accelrys,Inc., San Diego, CA, USA) (26). Common pharmacophore featureswere identified based on 34 training set compounds of known

679

Chem Biol Drug Des 2011; 78: 679–688

Research Article

ª 2011 John Wiley & Sons A/S

doi: 10.1111/j.1747-0285.2011.01202.x

mPGES-1 inhibitory activity (27), and low-energy conformations foreach compound were generated using FAST generation module inDS2.5. Statistical validation of the pharmacophore was performed viaCatScramble program. The critical pharmacophore features identifiedwere used to screen the TCM database for candidates using theLigand Pharmacophore Mapping protocol of DS 2.5.

Bioactivity prediction modelsSupport vector machine (SVM) and multiple linear regression (MLR)were established as activity prediction models using 185 compounds(148 training sets, 37 test sets) associated with mPGES-1 inhibitionfrom previous studies (27–32). Predicted activities were described aspIC50 [log(1 ⁄ IC50)]. LibSVM constructed by Chang and Lin (33) wasused for SVM model, and MATLAB (34) was used for MLR model. Thesuitable descriptors used for both prediction models were indicatedby the genetic function approximation (GFA) module (35) with the esti-mation of square correlation coefficient (R2). Both prediction modelswere validated using 5-fold cross-validation test.

Molecular dockingCandidates from pharmacophore screening were docked into theglutathione binding site of mPGES-1 (PDB: 3DWW) (36) with the

force field of CHARMm (37) using LigandFit module (38) of DS 2.5.MF63, the orally active inhibitor (39), was used as the control. Thecandidates were ranked by Dock score (38).

Molecular dynamics simulationSimulation package of DS 2.5 (40) was used for molecular dynam-ics (MD) simulation of the ligand–protein complexes with theforce field of CHARMm (37). Minimization for each complex wasperformed by steepest descent (41) and conjugate gradient (42)for 1000 cycles, respectively. The SHAKE algorithm was used withtime step intervals of 0.002 pseconds for whole MD stages. Mini-mized samples were gradually heated from 50 to 310 K within50 pseconds, equilibrated for 200 pseconds, and followed by a30-nseconds production procedure with the NVT canonical ensem-ble. Analyze Trajectory module in DS 2.5 was used for analyzingthe MD trajectory of each protein–ligand complex and individualligands.

Figure 1: Flowchart of experimental design.

Table 1: Top ten pharmacophore hypotheses generated by Hypo-Gen

HypothesisTotalcost

Costdifference

Errorcost RMS R value Feature

1 153.99 43.77 141.30 1.259 0.823 DYRR2 154.28 43.48 140.70 1.245 0.828 DYRR3 156.72 41.04 144.32 1.327 0.801 ADYR4 157.38 40.37 144.95 1.341 0.796 DRR5 158.46 39.30 146.25 1.369 0.786 DRR6 160.14 37.62 146.00 1.364 0.788 DYRR7 160.55 37.21 146.26 1.370 0.787 DYRR8 160.56 37.20 146.14 1.367 0.788 ADHYY9 160.64 37.11 147.71 1.401 0.775 ADYR

10 160.93 36.83 144.10 1.323 0.803 ADYYY

Null cost: 197.76. Fixed cost: 126.56. Configuration cost: 11.07. A, D, H, Y,and R represent hydrogen bond acceptor, hydrogen bond donor, aliphatichydrophobic, aromatic hydrophobic, and aromatic ring features, respectively.

0 1 2 3 4 5 6 7 8 9 10150

160

170

180

190

200

210

220

230

240

250

Tota

l cos

t

Hypotheses

Initial Random1 Random2 Random3Random4 Random5 Random6 Random7Random8 Random9 Random10 Random11Random12 Random13 Random14 Random15Random16 Random17 Random18 Random19

Figure 2: Validation of HypoGen hypotheses by CatScramble.

Chen et al.

680 Chem Biol Drug Des 2011; 78: 679–688

Results and Discussions

Pharmacophore hypotheses generation(HypoGen)The hypotheses of mPGES-1 generated by HypoGen algorithm werelisted in Table 1. Pharmacophore properties identified by the Hypo-Gen protocol include hydrogen bond acceptor (A), hydrogen bonddonor (D), aliphatic hydrophobic (H), aromatic hydrophobic (Y), andaromatic ring (R) features. Because top hypothesis generated a costdifference of 43.77, a 75–90% correlation to true data was expected.HypoGen results were further validated by CatScramble method (Fig-ure 2), where total costs of the initial generated hypotheses were

much lower than the random data sets. The top HypoGen hypothesiswas selected as the pharmacophore based on the large cost differ-ence (43.77), low root-mean-squared deviation (RMSD) (1.259), andhigh correlation coefficient (0.823). Pharmacophore features identifiedinclude one hydrogen bond donor, one aromatic hydrophobic, and twoaromatic rings for pi-interaction. Pharmacophore mapping of the tophypothesis indicates that potential inhibitors should have phenylgroups for pi-interactions with the mPGES-1 protein.

The validated pharmacophore was used to screen the TCM Data-base@Taiwan for compounds with similar features. Shanciol A, shan-ciol B, castilliferol, aurantiamide acetate, and the control compound

A

HN

N

Cl

N

NMF63

B

OH

O

OHHO

O

O

Shanicol B

C

OHO

OHHO

O

O

OShanicol A

D

O

OH

OHOO

O

OH

HO

Castilliferol

E

OHN

HN

O

OO

Aurantiamide acetate

Figure 3: Scaffolds of control and traditional Chinese medicine candidate compounds. (A) MF63, (B) Shanciol B, (C) Shanciol A, (D) Castil-liferol, and (E) Aurantiamide acetate.

B DA C E

Figure 4: Pharmacophore mapping for (A) MF63, (B) Shanciol B, (C)Shanciol A, (D) Castilliferol, and (E) Aurantiamide acetate. Key featuresinclude pi interactions (orange), hydrogen bond donor (purple), and hydrophobic (light blue).

4

5

6

7

8

9

10

Pre

dict

ed a

ctiv

ity(M

LR)

2 = 0.8661Test set

4 5 6 7 8 9 10Observed activity

Training set RA

4 5 6 7 8 9 104

5

6

7

8

9

10

Pre

dict

ed a

ctiv

ity(S

VM

)

Observed activity

Training set R2 = 0.8393Test set

B

Figure 5: Correlation of observed activity (pIC50) and predicted activity (pIC50) using (A) MLR, and (B) support vector machine.

Investigation into Potent Inflammation Inhibitors

Chem Biol Drug Des 2011; 78: 679–688 681

MF63 (Figure 3) had good alignment with the HypoGen pharmaco-phore (Figure 4) and were used in subsequent modeling trials. Shanci-ol A and shanciol B are compounds isolated from the pseudobulb ofAppendiculate cremastra which has heat clearing and detoxicatingproperties. Castilliferol is isolated from dried Centella asiatica, andaurantiamide acetate originates from the dried bud of Daphne gen-kwa. Both TCM compounds are used to clear heat and dry dampness.

Bioactivity prediction modelsRepresentative descriptors selected by GFA were the following:ES_Sum_ssssC, ES_Sum_tN, ES_Sum_sF, ES_Count_tN, Num_Ex-

plicitBonds, Num_RotatableBonds, BIC E_DIST_equ, IAC_Mean, IC,V_ADJ_mag, V_DIST_mag, CHI_2, Wiener, Kappa_1, Kappa_2_AM,SC_1, Jurs_DPSA_3, Jurs_PNSA_2, and Shadow_XY. An MLRmodel with the nineteen descriptors was established with a trainingset of 148 compounds. In addition, a test set of 37 compounds wasused to evaluate the constructed model. The following multiple lin-ear model was obtained:

Table 2: Docking results and predicted biological activities oftop traditional Chinese medicine compounds and control

NameDockingscore

Predicted pIC50

MLR

Supportvectormachine

Shanciol B 63.546 6.58 5.93Shanciol A 41.137 6.35 5.78Castilliferol 35.820 6.14 6.19Aurantiamide acetate 29.871 4.72 5.21MF63a 27.838 9.44 9.22

aControl.

A B C

D E

Figure 6: Docking poses of (A) MF63, (B) Shanciol B, (C)Shanciol A, (D) Castilliferol, and (E) Aurantiamide acetate in mPGES-1 bindingsite.

0.8

1.0

1.2

1.4

1.6

1.8

2.0

Who

le m

olec

ule

RM

SD (Å

)

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 300.0

0.4

0.8

1.2

1.6

Liga

nd R

MSD

(Å)

MF63Shanciol BShanciol ACastilliferolAurantiamide acetate

Time (ns)

A

B

Figure 7: Root-mean-squared deviation profiles of (A) ligand-protein complex, and (B) ligands of MF63, Shanciol B, Shanciol A,Castilliferol, and Aurantiamide acetate in complex with mPGES-1.

Chen et al.


The SVM model was established with the same training set andtest set. The statistical significances of the two models wereshown in Figure 5, and the square correlation coefficients (R2) forthe training set were 0.8661 and 0.8393 for MLR and SVM models,respectively. The predicted pIC50 of each ligand in the training setand test set using the MLR and SVM models were listed inTables S1 and S2, respectively. Reasonable bioactivities were pre-dicted for the four TCM compounds and the control, MF64 (Table 2).The predicted pIC50 values of MF63 by MLR and SVM models are9.44 and 9.22, respectively. The predicted pIC50 values by MLRmodel for TCM ligands, shanciol B, shanciol A, castilliferol, and au-rantiamide acetate are 6.85, 6.35, 6.14, and 4.72, respectively. Thepredicted pIC50 values by SVM model for TCM ligands, shanciol B,shanciol A, castilliferol, and aurantiamide acetate were 5.93, 5.78,6.19, and 5.21, respectively.

Predict IC50 ¼ �67:4923þ 0:0840� ES Sum ssssCþ 0:9315� ES Sum tNþ 0:0285� ES Sum sF

� 8:0527� ES Count tN� 0:0383� Num ExplicitBonds� 0:4295� Num RotatableBonds

þ 28:1609� BICþ 0:0032� E DIST equ� 2:9090� IAC Mean� 4:2697� IC

� 1:3664� V ADJ magþ 0:0048� V DIST magþ 0:3846� CHI 2� 0:0023�Wiener

� 1:5286� Kappa 1þ 0:7200� Kappa 2 AMþ 18:1270� SC 1� 0:0511� Jurs DPSA 3

� 0:0032� Jurs PNSA 2þ 0:0397� Shadow XY

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30–17 000–16 900–16 800–16 700–16 600–16 500–16 400–16 300–16 200–16 100–16 000–15 900–15 800–15 700

Time (ns)

Tota

l ene

rgy

(kca

l/mol

)

MF63Shanciol BShanciol ACastilliferoAurantiamide acetate

Figure 8: Total energy profiles of mPGES-1 in complex withMF63, Shanciol B, Shanciol A, Castilliferol, and Aurantiamide ace-tate.

A B C

ED

Figure 9: The docking poses of mPGES-1 with (A) MF63, (B) Shanciol B, (C) Shanciol A, (D) Castilliferol, and (E) Aurantiamide acetate at30 nseconds of MD. White dashed lines indicate H-bond interactions between the ligand and protein chain A (light red) or chain B (lightblue). Light yellow lines represent pication interactions.



Molecular dockingThe scoring function results of four TCM compounds and control,MF64, after docking were listed in Table 2, and the scoring func-tion, Dock Score (38), was used to rank the docking results. As

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 301.5

2.0

2.5

3.0

3.5

4.0

Dis

tanc

e (Å

)

Time (ns)

A:Arg73:HE - MF63:N17A:Arg73:HH21 - MF63:N17

Figure 10: H-bond interactions and distances between mPGES-1 and MF63.

Table 3: H-bond interactions and distances of mPGES-1 with MF63 and traditional Chinese medicine candidates

Ligand H-bondLigandatom Amino acid

H-bond Distance (�)H-bondoccupancya (%)Max. Average Min.

MF63 1 N17 A:Arg73:HE 3.65 3.03 2.43 000.202 N17 A:Arg73:H21 3.78 2.83 1.72 023.533 N28 A:Arg73:H21 5.93 4.58 2.18 004.07

Shanciol B 1 O31 A:Arg70:HH11 5.70 3.48 2.15 003.002 O31 A:Arg70:HH12 5.17 3.52 2.25 000.333 O31 A:Arg70:HH22 6.19 3.80 1.72 032.004 O16 A:Arg73:HE 4.52 3.52 2.47 000.075 O7 A:Arg110:HE 5.15 3.27 2.19 000.606 H37 B:Tyr28:OH 5.22 2.66 1.79 052.807 H48 B:His72:O 4.29 2.41 1.72 076.408 H48 B:Met76:SD 5.54 3.08 2.05 012.40

Shanciol A 1 O16 A:Arg73:HH11 6.06 3.07 2.39 000.202 O28 A:Arg126:HH12 4.07 2.66 1.85 037.273 O28 A:Arg126:HH22 2.26 1.81 1.58 100.004 O32 B:Arg38:HH22 2.92 2.31 1.85 086.135 H52 B:His53:O 4.14 2.88 2.15 004.476 O30 B:Gly54:HN 7.67 3.36 2.36 000.207 H52 B:Ser61:OG 4.49 2.74 2.13 010.20

Castilliferol 1 O11 A:Arg70:HH21 3.67 2.14 1.60 87.402 O17 A:Arg73:HH11 3.99 3.07 2.19 001.533 H48 A:Glu77:OE1 5.14 3.21 1.91 049.004 H48 A:Glu77:OE2 4.81 3.16 2.17 016.675 O32 A:Arg126:HH12 3.46 2.31 1.70 073.736 O10 B:Arg38:HH22 3.07 2.13 1.70 095.207 H35 B:Leu51:O 3.93 2.14 1.68 088.278 O13 B:Cys68:HG 6.37 3.30 2.24 001.409 O15 B:Cys68:HG 5.20 2.62 1.91 051.73

10 O13 B:Arg73:HH11 5.01 3.40 2.04 006.6711 H43 C:His72:NE2 3.17 2.35 1.86 074.60

Aurantiamide acetate 1 O33 A:Arg70:HH11 4.08 3.32 2.27 002.332 O30 A:Arg73:HH11 3.20 2.44 1.66 051.603 O8 A:Arg73:HH12 3.64 2.49 1.72 053.80

aH-bond occupancy cutoff: 2.5 �.

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30

23456

Time (ns)

Shanicol B:H37 - B:Tyr28:OHShanicol B:H48 - B:His72:OShanicol B:H48 - B:Met76:SD

2

3

4

5

Dis

tanc

e (Å

)

A:Arg73:HE - Shanicol B:O16A:Arg110:HE - Shanicol B:O7

123456789

A:Arg70:HH11 - Shanciol B:O31A:Arg70:HH12 - Shanicol B:O31A:Arg70:HH22 - Shanicol B:O31

A

B

C

Figure 11: H-bond interactions and distances between mPGES-1 and Shanicol B.

Chen et al.


illustrated in Figure 6, pi–cation interactions exist in all the TCMligands and control. Each ligand has pi–cation interactions with atleast three different residues of mPGES-1. The docking poses sug-gest that Arg38, Arg70, Arg73, and Arg126 were important residuesin mPGES-1 for potential inhibitors to build pi–cation interactionswith. Pi–cation interactions with Arg70 of chain A and at least oneArg73 were formed in all the ligands. Shanciol A, castilliferol, andMF 64 also have pi–cation interaction with Arg38 of chain B.Instead of pi–cation interactions, shanciol B and aurantiamide ace-tate have hydrogen bond (H-bond) interactions with Arg38 of chainB. The Arg126 of chain A has pi–cation interactions with shanciol Band castilliferol and has H-bonds with shanciol A.

MD simulationMD simulation was performed after docking to further analyze theinteractions between protein and ligand. Root-mean-squared devia-tion was used to study atomic fluctuations during MD simulation.Whole-molecule RMSD values for ligand–mPGES-1 complexes (Fig-

ure 7A) and ligand RMSD values for each ligand (Figure 7B)reached stability after 23 ns. The total energy for each protein–ligand complex (Figure 8) stabilized after 27 ns. Pi–cation interac-tions were stable for each protein–ligand complex after 30 ns ofMD (Figure 9). H-bonds of mPGES-1 with MF63 and TCM candi-dates were summarized in Table 3. Lower than expected H-bondoccupancy rates were observed and were likely because H-bondslonger than 2.5 � were not taken into account. As pi–cation inter-actions are strong ionic bonds, ligand–protein bindings and H-bonddistances remained stable throughout analysis. Each ligand had astable H-bond with Arg73 of chain A. In addition, the distances forkey residues Arg38, Arg70, Arg73, and Arg126 were also stable fol-lowing MD (Figure 10–14). In Figure 11C, the hydrogen bond dis-tance between shanciol B and His72, Met76 of chain B fluctuatedduring 8–11 nseconds of MD simulation. These sharp fluctuationswere because of the rotation of the hydroxide group (-OH) of shan-ciol B (Movie S1). Figure 15 illustrates the key features identifiedfor potential inhibitor binding with mPGES-1. Phenyl groups shouldbe available to form pi–cation interactions with residues Arg38,

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30

2

3

4

5

6

7 B

Time (ns)

B:Arg38:HH22 - Shanicol A:O32 Shanicol A:H52 - B:His53:O B:Gly54:HN - Shanicol A:O30 Shanicol AA:H52 - B:Ser61:OG

2

3

4

5

6 A

Dis

tanc

e (Å

) A:Arg126:HH12 - Shanicol A:O28 A:Arg73:HH11 - Shanicol A:O16

Figure 12: H-bond interactions and distances between mPGES-1 and Shanicol A.

1

2

3

4

5A:Arg70:HH21 - Castilliferoll:O11A:Arg73:HH11 - Castilliferol:O17

A

23456 C

Dis

tanc

e (Å

) Castilliferol:H48 - A:Glu77:OE1Castilliferol:H48 - A:Glu77:OE2

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28

2

3

4E A:Arg126:HH12 - Castilliferol:O32

B B:Arg38:HH22 - Castilliferol:O10Castilliferol:H35 - B:Leu51:O

D B:Cys68:HG - Castilliferol:O13B:Cys68:HG - Castilliferol:O15B:Arg73:HH11 - Castilliferol:O26

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30

F

Time (ns)

Castilliferol:H43 - C:His72:NE2

Figure 13: H-bond interactionsand distances between and Castil-liferol.

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30

2

3

B

Time (ns)

Dis

tanc

e (Å

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A:Arg73:HH11 - Aurantiamide acetate:O30A:Arg73:HH12 - Aurantiamide acetate:O8

2

3

4A:Arg70:HH11 - Aurantiamide acetate:O33A

Figure 14: H-bond interactions and distances between mPGES-1 and Aurantiamide acetate.



Arg70, Arg73, and Arg126, and hydrogen bond donors are alsoimportant to stable ligand–protein binding. These key features iden-tified by MD correspond to those identified by 3D-QSAR. Bothmethods highlight the importance of pi–cation interactions andhydrogen bond donors.

Conclusion and future direction

Structural analyses identify pi-interactions and H-bond donors askey features of m-PGES inhibitors. Potential inhibitors should formpi–cation interactions with mPGES-1 at the key residues, Arg38,Arg70, Arg73, and Arg126. Because mPGES-1 is rich in arginineamino acids, the availability of phenyl groups becomes importantcriteria for potential mPGES-1 inhibitors.

TCM compounds, shanciol B, shanciol A, castilliferol, and aurantia-mide acetate, have structural characteristics that match the phar-macophore map, are biologically active, and can form stablecomplexes with mPGES-1. Hence, we propose TCM candidates,shanciol B, shanciol A, castilliferol, and aurantiamide acetate, aspotential mPGES-1 inhibitors.

Acknowledgments

The research was supported by grants from the National ScienceCouncil of Taiwan (NSC 99-2221-E-039-013-), Committee on ChineseMedicine and Pharmacy (CCMP100-RD-030), China Medical Univer-sity and Asia University (CMU98-TCM, CMU99-TCM, CMU99-S-02,CMU99-ASIA-25, CMU99-ASIA-26 CMU99-ASIA-27 CMU99-ASIA-28). This study is also supported in part by Taiwan Department of

Health Clinical Trial and Research Center of Excellence (DOH100-TD-B-111-004) and Taiwan Department of Health Cancer ResearchCenter of Excellence (DOH100-TD-C-111-005). We are grateful tothe Asia University cloud-computing facilities.

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Supporting Information

Additional Supporting Information may be found in the online ver-sion of this article:

Table S1. Observed and predicted activities (pIC50) of (a) 148training set compounds, and (b) 37 test set compounds as predictedby MLR.

Table S2. Observed and predicted activities (pIC50) of (a) 148training set compounds, and (b) 37 test set compounds as predictedby SVM.

Movie S1. Molecular dynamics insights on sharp H-bond fluctua-tions observed in Shanicol B.

Please note: Wiley-Blackwell is not responsible for the content orfunctionality of any supporting materials supplied by the authors.Any queries (other than missing material) should be directed to thecorresponding author for the article.

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Investigation into Potent Inflammation Inhibitors from Traditional Chinese Medicine

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