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Article

Biological Evaluation and 3D-QSAR Studies of Curcumin Analogues as Aldehyde Dehydrogenase 1 Inhibitors

1
School of Light Industry and Chemical Engineering, Guangdong University of Technology, Guangzhou 510500, China
2
Guangzhou Improve Medical Technology Co., Ltd., Guangzhou 510530, China
3
Susan Lehman Cullman Laboratory for Cancer Research Ernest Mario School of Pharmacy, Rutgers, the State University of New Jersey, Piscataway, NJ 08854, USA
*
Author to whom correspondence should be addressed.
Int. J. Mol. Sci. 2014, 15(5), 8795-8807; https://doi.org/10.3390/ijms15058795
Submission received: 4 January 2014 / Revised: 13 March 2014 / Accepted: 24 March 2014 / Published: 16 May 2014
(This article belongs to the Section Physical Chemistry, Theoretical and Computational Chemistry)

Abstract

:
Aldehyde dehydrogenase 1 (ALDH1) is reported as a biomarker for identifying some cancer stem cells, and down-regulation or inhibition of the enzyme can be effective in anti-drug resistance and a potent therapeutic for some tumours. In this paper, the inhibitory activity, mechanism mode, molecular docking and 3D-QSAR (three-dimensional quantitative structure activity relationship) of curcumin analogues (CAs) against ALDH1 were studied. Results demonstrated that curcumin and CAs possessed potent inhibitory activity against ALDH1, and the CAs compound with ortho di-hydroxyl groups showed the most potent inhibitory activity. This study indicates that CAs may represent a new class of ALDH1 inhibitor.

1. Introduction

The aldehyde dehydrogenase 1 (ALDH1) family is mainly present in the cytoplasm of various tissues and catalyzes the oxidation of aliphatic and aromatic aldehydes to the corresponding carboxylic acids in the presence of NAD or NADP as cofactor [1]. It plays an important role in the detoxification of peroxidic aldehydes produced by ultraviolet light absorption, protecting the lens of the eye [2]. Recently, the role of ALDH1 in drug resistance was observed in the case of cyclophosphamide chemotherapy of cancer cells with high level of expression of ALDH1 [3,4]. It is reported that the oxidation of aldophamide could be accomplished directly by ALDH1, and downregulation of ALDH1 by antisense RNA could result in increasing the sensitivity of tumour cells to 4-hydroperoxy-cyclophosphamide (4-HC), an active derivative of cyclophosphamide [5]. Decreasing the ALDH1 protein level or blocking the enzyme activity led to an increase in the sensitivity of chemotherapy [6]. It was also demonstrated that ALDH1 activity was related to metastatic potential in murine OS cells [7,8] and ALDH1 inhibitors induced apoptosis in the lymphoid cell line BAF3H16 over expressing the bcl2 gene [911]. Therefore, inhibiting ALDH1 activity in tumor cells may be a strategy to alleviate chemoresistance and induce apoptosis in some cancer cells.
Curcumin is a natural occurred compound which is extracted from rhizome of Zingiberaceae Turmeric. Curcumin has shown antioxidant, anti-inflammatory, antiviral, antibacterial, antifungal and anticancer activities [12]. Our research group reported that curcumin and its analogues synthesized in our laboratory [13] possessed potent inhibitory activities on PC-3, Panc-1, and HT-29 cells, which have high expression of ALDH [14]. These suggest that curcumin analogues (CAs) may serve as a new class of ALDH1 inhibitors. In this paper, the inhibitory activity, mechanism mode, molecular docking and 3D-QSAR of curcumin analogues against ALDH1 were studied.

2. Results and Discussion

2.1. Activity Assay

The structure and activity of CAs data were showed in Table 1. Analysis of structure activity relationship indicated that the change of glutaric enone and phenyl substituents had important influence on the activity of the compounds. The activity data pIC50 is range 4.13 to 5.46. Comparing six different glutaric ketones, the CAs have a higher activity when the X group is cyclopentanone. When the glutaric enone is the cyclopentanone and the phenyl substituent is an electron-withdrawing group, the CAs have a higher activity. At the same time, the curcumin and disulfiram, which to ALDH1 inhibitory activity, show the data IC50 values of 36.9 and 2.91 μmol/L, respectively. The activity of compound 6 (IC50 3.41 μmol/L) is over 10 times higher than that of curcumin and similar to disufiram. Therefore, curcumin analogues may serve as a new class of ALDH1 inhibitors.

2.2. Kinetic Analysis of Selected Compounds on ALDH1

The inhibitory mechanisms of selected compound 6 and 24 against ALDH1 during the oxidation of proponal were determined by the same methods. Double-reciprocal plots of the inhibition kinetics of selected compounds against ALDH1 are shown in Figure 1. Compound 6 and 24 both were mixed-competitive inhibition type, as illustrated in Figure 1. The Michaelis constant (km) of Compound 6 and 24 is 36.6 and 24.5 μM, respectively.

2.3. CoMFA and CoSIA Statistical Results

It is well known that the CoMFA and CoMSIA models are alignment sensitive, and the quality and the predictive ability of the models are directly dependent on the alignment rules. 3D-QSAR model with a q2 value > 0.5 and r2 > 0.9 are considered statistically significant and highly self-consistent, respectively. The statistical results of CoMFA and CoMSIA are shown in Table 2. The optimal number of components depend on selecting the highest q2 value. By PLS analysis result a high q2 value of 0.606 with 9 components for CoMFA. The non-cross-validated PLS analysis results in a conventional r2 0.999; F 2577.847 and a standard error of estimation (SEE) of 0.011, the steric and electrostatic contributions were found to be 55.2% and 44.6% respectively.
Table 2, shows the PLS results of CoMSIA analysis using different combinations. The SED field descriptors exhibited highest q2, better SEE and F values than the others. Therefore combinations of steric (S), electrostatic (E), and hydrogen bond donor (D) fields was selected as the best model. The CoMSIA model q2 of 0.56 with an optimized component number of 6 with a low SEE of 0.031 and F value of 210.105. The steric, electrostatic contributions and hydrogen bond donor were found to be 28.7%, 37.8% and 33.8%.
The ultimate characteristic of the 3D-QSAR technique is the validation of the externally driven 3D-QSAR model by means of calculating quantitatively the activities of test set compounds. The predicted activities for the inhibitor versus their experimental activities are listed in Table 3. Test sets are generally used to evaluate the external predictive capabilities of QSAR models. The correlation between predicted activities and the experimental activities of CoMFA and CoMSIA model is plotted in Figure 2. It is good linear relationship between the predicted and experiment activities of the dataset. Among them, compound 24 is found to be an outlier with residual values of 0.115 and 0.334 for CoMFA and CoMSIA model, respectively. There are numerous reasons for the presence of outliers, such as incorrectly experimental values or non-representative sampling designs. The solubility of compound 24 is not good, the errors may be relatively large.

2.4. Contour Maps Analysis

2.4.1. CoMFA Contour Maps

CoMFA steric and electrostatic contour maps are shown on compound 6 (the highest inhibitory activity) as the template. The steric fields are represented by green and yellow colored contours, in which green areas indicate regions where increased steric hindrance would increase the ALDH1 inhibitory activity, whereas the yellow areas suggest regions where the bulky groups are not favored. From the Figure 3A static fields, a large green contour overlaps the substituent group of R1 position that illustrates increasing bulky substituent is helpful to increase activity of inhibitors, as compounds 18, 19, 20 have more potent inhibitory activity than compound 17.
The electrosteric fields are represented by blue and red contours and depict the position where positively charged groups and negatively charged groups would be beneficial to the inhibitory activities, respectively. From the Figure 3B electrostatic field, near the R1 substituent and phenyl ring 2 area M2 is a large blue contour respectively that ring M2 area indicates an increasing inhibitory negatively charged group. A large red contour near the phenyl indicates positive charged groups increasing inhibitory activity and compounds 18, 19, 20 show more potent inhibitory activity than compound 21.

2.4.2. CoMSIA Contour Map

CoMSIA steric, electrostatic and hydrogen-bond donor contour maps are shown also on compound 6 (the highest inhibitory activity) as the template. From the Figure 4A static and Figure 4B electrostatic fields, the distribution of the area M1, area M2 and area M3 are almost consistent with the CoMFA models. In the hydrogen-bond donor field, the cyan contours, represent the hydrogen bond-donating groups increasing the activity, the purple contour decreasing the activity. From the Figure 4C, the cyan contours are near the phenyl ring hydroxyl groups. Introducing hydroxyl groups on the phenyl ring, which can improve activity.

2.5. Binding Model Analysis

In order to clarify the combination of CAs with ALDH1 and determine the stability of the 3D-QASR modes, selecting the most active compound 6 with ALDH1 for Surflex-Dock. The Surflex-Dock score of compound 6 is 7.34 and that score showed that the in vitro tested result is consistent with the molecular docking. Figure 5A is the binding mode of A2 with active sites of ALDH1. Compound 6 was mainly surrounded by active pocket included in the residues of Cys301, Ile303, Gly245, Thr244, Phe243, Asn169, Trp168 and so on. Compound 6 carbonyl O and OH respectively formed hydrogen bond with NH2 of Tpr168 (Å 2.511) and NH2 of Asn 169 (Å 2.208) located inside the pocket, which has important inhibitory activity towards ALDH1. Besides compound 6 also formed hydrogen bond with Ser246 (Å 2.220) outside the activity pocket. Trp168 is important to form a π bond with compound 6 glutaric enone. From the Figure 5B, MOLCAD lipophilic potential (LP) showed that the glutaric enone (area M1) and phenyl ring 2 (area M2) are closed to the hydrophobic region and indicate increased hydrophobic group favor to improve inhibitory activity. This conclusion is consistent with the CoMSIA hydrophobic contour group. From the Figure 5C MOLCAD hydrogen bonding sites of the binding surfaces, the hydrophobic pocket has presented several hydrogen donors and acceptors. While the compound 6 formed three hydrogen bonds just as an acceptor, increasing the inhibitor hydrogen donor may strengthen the inhibitory activity.
Figure 6A is the binding mode of curcumin with active sites of ALDH1. Although curcumin is able to deep into the pocket, which just formed hydrogen with Gly245 outside the pocket, and the collision is very high. From the active site MOLCAD surface representation Liphilic potential and Hydrogen Bonding, we find that curcumin can not form hydrogen bonds in the active pocket and the skeleton diphenyl ketone of curcumin is too large, which is unfavorable combination with ALDH1. According to molecular docking and 3D-QSAR, a series of novel derivatives were designed. The activities of newly designed virtual molecules were predicted using CoMFA, CoMSIA models and the results were shown in Table 4.

3. Materials and Methods

3.1. Materials

Aldehyde dehydrogenase, NAD, dimethyl sulfoxide and propanal were all purchased from Sigma (St. Louis, MO, USA). All other reagents were analytical reagents from Sinopharm Chemical Reagent Co., Ltd. (Shanghai, China). Curcumin analogues 1–30 comes from our study group [15].

3.2. Measurement of ALDH1 Activity and Data Set

In vitro ALDH1 inhibitory assay was performed as follows [9,10,16]. To a reaction mixture containing 1 mM EDTA, 100 mM KCl, 2 mM NAD, 0.1 M sodium phosphate buffer pH 7.4, 1 U/mg baker’s yeast ALDH1 was added by Multifunction microplatereader (Tecan Infinite 200, TECAN Austria GmbH, Männedorf, Switzerland). The reaction was started by the addition of 1 mM propanal in final volume of 300 μL and the optical density (OD) was read at 340 nm at 0 min (just after the addition of the substrate) and reacted for 5 min. The enzyme activity is expressed as OD/min at 25 °C at the pH 7.4. Results are the average of three experiments done in duplicate. The structures of the compounds and their biological data are given in Table 1. IC50 (inhibit growth by 50%). The IC50 values in units of μM were transformed in pIC50 (–log IC50). The data was randomly divided into training set (26 samples) and test set (4 samples * carry out external validation).

3.3. Molecular Modeling and Alignment

Minimize molecular were performed using SYBYL 8.0 package (Tripos, Shanghai, China). All structures were minimized with the Tripos force field, added the Gasteiger–Hückle. Powel optimize the energy gradient, the maximum times to 2000 times the energy convergence criterion reached 0.005 kcal mol−1, and got its 20 small molecule ligand conformations. The most potent CA (compound 6) was selected as the alignment template molecular. Selecting the appropriate common substructure, the 26 compounds were next aligned. Finally, 26 compounds were aligned to a common substructure of the template using the “align database” command (Figure 7) [17].

3.4. 3D-QSAR Models

3D-QSAR models were constructed by using CoMFA, CoMSIA methods. Parameters of CoMFA and CoMSIA were the default values. The cutoffs value was set 30 kcal/mol. With standard options for scaling of variables, the regression analysis was performed using the “leave-one-out” cross-validation partial least squares method (PLS) and use samples, resulted q2 and optimum components [18,19]. The next non-cross-validated model was developed with a no validation PLS analysis. CoMSIA method was performed using steric, electrostatic, hydrophobic, hydrogen-bond donor and hydrogen-bond acceptor descriptors. In this study, the common skeleton diphenyl ketone was split into three pieces by cutting two single bonds (Figure 8).

3.5. Molecular Docking

To assess the potent binding conformations and find more insight into the understanding of the interactions of inhibitor, molecular docking analysis was used to the Surflex Dock in SYBYL8.0 [20]. The crystal structure of ALDH1 was retrieved from RCSB Protein Data Bank (PDB: 1BXS) [21]. The crystal structure includes the two dimers, comprised of A, B, C, D four single chain, deleted chain C, B, D and all water molecules. Biopolymer module was then used to repair the crystal structure of the protein termini treatment, fix side chain amides, residues and add charges. By using the Surflex-Docking mode, the potent CAs docking with ALDH1, selected Cys302 as active site and the threshold 0.5, the active pocket formed by computing, the others are the default settings.

4. Conclusions

Using in vitro assays for inhibitory effect of CAs on ALDH1, compound 6, 7, 8, and 24 exhibited high inhibitory activity. Therefore there is a theory that new ALDH1 inhibitors can be found from the CAs. Meantime, based on the molecular docking results, the 3D-QSAR modeling has developed an understanding of the relationship of molecular structure with ALDH1 inhibitory activity and used a 3D-QSAR model to design a set of novel curcumin analogues with predicted activities. The results indicate that CAs can be a potent ALDH1 inhibitor.

Acknowledgments

This work was financially supported by National Natural Science Foundation of China (21272043, 81272452), Projects of the Ministry of Education and Guangdong Province for Production-Study-Research Integration (2012B091000170, 2012B091100342), and Project of Guangzhou Science & Technology International Collaboration (2013J4500014), The China Postdoctoral Science Foundation (2013M540649).

Conflicts of Interest

The authors declare no conflict of interest.
  • Author ContributionsZhiyun Du and Xi Zheng got the idea; Hui Wang, Yan He and Qiuyan Zhang did the experiment; Hui Wang, Changyuan Zhang and Zhikai Zhang analyzed the data; Hui Wang, Zhiyun Du and Jun Zhao wrote the paper.

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Figure 1. Lineweaver–Burk plots for inhibition of compound 6 and compound 24 against aldehyde dehydrogenase 1 (ALDH1) for the catalysis of propanal.
Figure 1. Lineweaver–Burk plots for inhibition of compound 6 and compound 24 against aldehyde dehydrogenase 1 (ALDH1) for the catalysis of propanal.
Ijms 15 08795f1
Figure 2. (A) The experimental and Predicted activities of CoMFA; (B) The experimental and Predicted activities of CoMSIA.
Figure 2. (A) The experimental and Predicted activities of CoMFA; (B) The experimental and Predicted activities of CoMSIA.
Ijms 15 08795f2
Figure 3. CoMFA steric filed (A) and electrostatic field (B). S fields: favored (green) and disfavored (yellow); E fields: electropositive (blue) and electronegative (red).
Figure 3. CoMFA steric filed (A) and electrostatic field (B). S fields: favored (green) and disfavored (yellow); E fields: electropositive (blue) and electronegative (red).
Ijms 15 08795f3
Figure 4. CoMSIA steric filed (A) and electrostatic field (B); CoMSIA Hydrogen bond donor (C). S fields: favored (green) and disfavored (yellow); E fields: Lipophilic (blue) and hydrophobic (red); D field: favored (purple) and disfavored (cyan).
Figure 4. CoMSIA steric filed (A) and electrostatic field (B); CoMSIA Hydrogen bond donor (C). S fields: favored (green) and disfavored (yellow); E fields: Lipophilic (blue) and hydrophobic (red); D field: favored (purple) and disfavored (cyan).
Ijms 15 08795f4
Figure 5. The binding mode between compound 6 with ALDH1 (A). Active site MOLCAD surface representation Liphilic potential (B) and Hydrogen Bonding (C); (B) Brown: Hydrogen and green: Hydrophlic; (C) Red: Hydrogen donor and blue: Hydrogen acceptor.
Figure 5. The binding mode between compound 6 with ALDH1 (A). Active site MOLCAD surface representation Liphilic potential (B) and Hydrogen Bonding (C); (B) Brown: Hydrogen and green: Hydrophlic; (C) Red: Hydrogen donor and blue: Hydrogen acceptor.
Ijms 15 08795f5
Figure 6. The binding mode between curcumin with ALDH1(A). Active site MOLCAD surface representation Liphilic potential and Hydrogen Bonding; (B) Brown: Hydrogen and green: Hydrophlic; (C) Red: Hydrogen donor and blue: Hydrogen acceptor).
Figure 6. The binding mode between curcumin with ALDH1(A). Active site MOLCAD surface representation Liphilic potential and Hydrogen Bonding; (B) Brown: Hydrogen and green: Hydrophlic; (C) Red: Hydrogen donor and blue: Hydrogen acceptor).
Ijms 15 08795f6
Figure 7. Molecular alignment of the compounds in the training set.
Figure 7. Molecular alignment of the compounds in the training set.
Ijms 15 08795f7
Figure 8. Molecular skeleton region.
Figure 8. Molecular skeleton region.
Ijms 15 08795f8
Table 1. The structures and bioactivity values of activity of curcumin derivatives.
Table 1. The structures and bioactivity values of activity of curcumin derivatives.
Ijms 15 08795f9
CompoundsXR1R2R3IC50 μmol/LpIC50
11-(4-Br-2-F)4-piperidinoneOCH3OHOCH331.24.51
21-(4-Br-2-F)4-piperidinoneOHOHH33.54.47
34-piperidinoneOCH3OHOCH330.04.52
44-piperidinoneOHOHH39.44.40
5AcetoneOHOHH23.64.63
6cyclopentanoneOHOHH3.415.46
7cyclohexanoneOHOHH6.55.20
8tetrahydropyran-4-onesOHOHH7.95.10
9tetrahydrothiopyran-4-oneOHOHH22.74.64
10cyclopentanoneHOHH54.24.20
11tetrahydropyran-4-onesHOHH39.74.41
12cyclopentanoneOHHOH43.24.36
13cyclopentanoneOCH3OHH24.24.62
14tetrahydrothiopyran-4-oneOCH3OHH31.34.50
15tetrahydropyran-4-onesOCH3OHOCH316.64.78
16tetrahydropyran-4-onesOCH3OHF53.24.27
17AcetoneBrOHBr25.84.59
18cyclohexanoneBrOHBr17.54.76
19tetrahydropyran-4-onesBrOHBr18.74.73
20tetrahydrothiopyran-4-oneBrOHBr17.74.75
21tetrahydrothiopyran-4-oneOCH3OCH3OCH363.14.20
22cyclohexanoneHN(CH3)2H73.54.13
23tetrahydrothiopyran-4-oneHN(CH3)2H69.44.16
24cyclohexanoneHBrH8.205.08

Ijms 15 08795f10
CompoundsXRIC50 μmol/LpIC50

25CyclopentanoneIjms 15 08795f1130.14.53
26tetrahydrothiopyran-4-oneIjms 15 08795f1210.64.97
27cyclohexanoneIjms 15 08795f1263.24.22
28CyclohexanoneIjms 15 08795f1273.44.13
29CyclopentanoneIjms 15 08795f1313.7.4.86
30CyclohexanoneIjms 15 08795f1318.44.74
Curcumin36.94.43
Disufiram2.915.54
Table 2. Summary of the partial-least-squares for the CoMFA/CoMSIA models.
Table 2. Summary of the partial-least-squares for the CoMFA/CoMSIA models.
Statisticalq2Nr2SEEFField Contribution

SEHDA
CoMFA0.60690.9990.0112577.8470.5520.448
CoMFA0.59780.9980.018990.0300.5540.446
SE0.60890.9990.0821924.9260.3860.614
SHE0.43460.9450.04593.120.2940.4200.287
SED0.5660.9870.031210.1050.2870.37850.338
SEA0.44330.8480.13440.8910.270.3880.342
SEHD0.47730.9020.10767.6880.2190.3050.1820.293
SEDA0.48450.9680.064121.3170.2070.2950.3010.197
SEHA0.38330.8620.12845.790.2070.3190.1960.278
SEHDA0.42140.9450.08389.8390.1660.2690.1390.2470.179
q2, Crossvalidated correlation coefficient using leave-one-out procedure; N, optimal number of principal components; r2, non cross validated correlation coefficient; F: F-test value; Steric (S) and Electrostatic (E) field from CoMFA; Steric (S), Eectrostatic (E), Hydrophobic (H), Donor (D), and Acceptor (A) fields from CoMSIA.
Table 3. (CoMFA)/(CoMSIA) predicted activity (pIC50) of compounds.
Table 3. (CoMFA)/(CoMSIA) predicted activity (pIC50) of compounds.
CompoundsActualCoMFACoMSIA


PredictedResiduesPredictedResidues
14.514.533−0.0234.540−0.030
24.474.4660.0044.4310.039
34.524.5030.0174.546−0.026
44.404.418−0.0184.433−0.033
54.634.704−0.0744.6300.000
65.345.341−0.0015.360−0.020
75.205.1070.0935.1090.091
85.105.0950.0055.102−0.002
9*4.644.763−0.1234.894−0.254
10*4.204.557−0.3574.404−0.204
114.414.503−0.0934.542−0.132
124.364.3180.0424.3330.027
134.624.674−0.0544.5280.092
144.504.4830.0174.533−0.033
15*4.784.5560.2244.919−0.139
164.274.307−0.0374.277−0.007
174.594.632−0.0424.598−0.008
184.764.825−0.0654.7540.006
194.734.6720.0584.733−0.003
204.754.7050.0454.6830.067
214.204.1380.0624.804−0.604
224.134.154−0.0244.133−0.003
234.164.1520.0084.1240.036
24*5.084.9650.1154.7460.334
254.534.540−0.014.646−0.116
264.974.8850.0854.6850.285
274.224.1990.0214.2020.018
284.134.1030.0274.131−0.001
294.864.8250.0354.885−0.025
304.744.7090.0314.808−0.068
*Test set.
Table 4. Designed molecules and predicted pIC50 values of ALDH1 inhibitors.
Table 4. Designed molecules and predicted pIC50 values of ALDH1 inhibitors.
(a)(b)(c)

Ijms 15 08795f14Ijms 15 08795f15Ijms 15 08795f16

CompoundTailR1R2R3R4R5R6Predict pIC50

CoMFACoMSIA
1aHOHHHOCH3H5.0164.858
2aHOHHHOCH3OCH35.8686.124
3aHOHHOCH3OCH3OCH35.9976.077
4aOHOHHHOCH3H6.1735.766
5aOHOHHOCH3OCH3H5.8156.096
6aOHOHHOCH3OCH3OCH35.6895.966
7aHBrHHOCH3H5.5426.079
8aHBrHOCH3OCH3H5.6895.993
9aHBrHOCH3OCH3OCH35.2575.857
10bHOHHHOCH3H5.6876.100
11bHOHHHOCH3OCH35.9746.240
12bHOHHOCH3OCH3OCH35.3705.725
13bOHOHHHOCH3H6.0626.127
14bOHOHHOCH3OCH3H6.0346.332
15bOHOHHOCH3OCH3OCH35.1415.739
16bHBrHHOCH3H6.1166.031
17bHBrHOCH3OCH3H6.0615.991
18bHBrHOCH3OCH3OCH35.0895.859
19cHOHHHOCH3H4.8004.785
20cHOHHHOCH3OCH36.1096.199
21cHOHHOCH3OCH3OCH35.9615.926
22cOHOHHHOCH3H6.0976.077
23cOHOHHOCH3OCH3H6.0625.914
24cOHOHHOCH3OCH3OCH35.7535.914
25cHBrHHOCH3H6.1686.128
26cHBrHOCH3OCH3H6.1046.253
27cHBrHOCH3OCH3OCH36.0285.842

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MDPI and ACS Style

Wang, H.; Du, Z.; Zhang, C.; Tang, Z.; He, Y.; Zhang, Q.; Zhao, J.; Zheng, X. Biological Evaluation and 3D-QSAR Studies of Curcumin Analogues as Aldehyde Dehydrogenase 1 Inhibitors. Int. J. Mol. Sci. 2014, 15, 8795-8807. https://doi.org/10.3390/ijms15058795

AMA Style

Wang H, Du Z, Zhang C, Tang Z, He Y, Zhang Q, Zhao J, Zheng X. Biological Evaluation and 3D-QSAR Studies of Curcumin Analogues as Aldehyde Dehydrogenase 1 Inhibitors. International Journal of Molecular Sciences. 2014; 15(5):8795-8807. https://doi.org/10.3390/ijms15058795

Chicago/Turabian Style

Wang, Hui, Zhiyun Du, Changyuan Zhang, Zhikai Tang, Yan He, Qiuyan Zhang, Jun Zhao, and Xi Zheng. 2014. "Biological Evaluation and 3D-QSAR Studies of Curcumin Analogues as Aldehyde Dehydrogenase 1 Inhibitors" International Journal of Molecular Sciences 15, no. 5: 8795-8807. https://doi.org/10.3390/ijms15058795

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