QSAR and docking studies of α,β-unsaturated carbonyl compounds against human breast adenocarcinoma cell line MCF-7 European Journal of Chemistry 9 (3) (2018) 275-280 European Journal of Chemistry View Journal Online View Article Online QSAR and docking studies of α,β-unsaturated carbonyl compounds against human breast adenocarcinoma cell line MCF-7 Maysoon Mohammed Almahdi 1,*, Ahmed Elsadig Mohamed Saeed 2 and Nadia Hanafy Metwally 3 1 Department of Chemistry, College of Education, Alzaiem Alazhari University, 1432, Khartoum, Sudan maysoon256@hotmail.com (M.M.A.) 2 Department of Chemistry, College of Science, Sudan University of Science and Technology, 407, Khartoum, Sudan asmsaeed@gmail.com (A.E.M.S.) 3 Department of Chemistry, Faculty of Science, Cairo University, 12613, Giza, Egypt nhmmohamed@yahoo.com (N.H.M.) * Corresponding author at: Department of Chemistry, College of Education, Alzaiem Alazhari University, 1432, Khartoum, Sudan. Tel: +249.9245.65006 Fax: +249.185.344511 e-mail: maysoon256@hotmail.com (M.M. Almahdi). 10.5155/eurjchem.9.3.275-280.1765 Received: 19 June 2018 Received in revised form: 11 July 2018 Accepted: 28 July 2018 Published online: 30 September 2018 Printed: 30 September 2018 A quantitative structure-activity relationships (QSAR) studies were carried out by correlating activity against human breast adenocarcinoma cell line MCF-7 of series of α,β- unsaturated carbonyl compounds with their physicochemical descriptors using multiple linear regression method. The predictability of the QSAR model was estimated using internal and external predictivity methods. The best QSAR model was selected, having the squared correlation coefficient r2 = 0.84684, correlation coefficient r = 0.9202, standard deviation s = 0.38484, and cross-validated squared correlation coefficient Q2 = 0.7621. Model obtained was used to predicted the activity against breast cancer for a set of designed α,β-unsaturated carbonyl compounds (A1-A16). Docking studies was performed for A1-A16 compounds to evaluated their inhibition on c-Met kinase, which has been overexpressed in a number of cancers. QSAR c-Met MCF-7 Cancer Molecular docking α,β-unsaturated compounds Cite this: Eur. J. Chem. 2018, 9(3), 275-280 Journal website: www.eurjchem.com 1. Introduction Cancer is a group of illness that results from cells in the body growing abnormally. These cells divide and produce new cells in an uncontrolled way that can spread throughout the body and cause damage to essential organs [1]. Despite recent advances in cancer therapy, cancer is still the second leading cause of death after cardiovascular disorders throughout the world [2], and it is projected to be the primary cause of death within the coming years [3]. Breast cancer is the most common type of cancer in women all over the world. Cancerous breast cells have recep- tors for binding with estrogen and progesterone to stimulate a growth responses [4]. Estrogens control the development and maintenance of the female sex organs, secondary sex charac- teristics, and mammary glands, as well as certain functions of the uterus and its accessory organs. They are also formed in the placenta in late pregnancy, which increases the spon- taneous activity of the uterine muscle and its response to oxytocic drugs [5]. Currently, there is a huge scientific and commercial interest in the discovery of potent, safe and selective anti- cancer medications. Among the currently identified antitumor agents, enone derivatives represent an important class of molecules that are abundant in edible plants [6]. Quantitative structure activity relationships and quantita- tive structure property relationship (QSPR) make it possible to predict the activities/properties of a given compound as a function of its molecular substituent. Essentially, new and untested compounds possessing similar molecular features are likewise assumed to also possess similar activities/ properties. Several successful QSAR/QSPR models have been published over the years which encompass a wide span of biological and physicochemical properties [7]. This method included data collection, molecular descriptor selection, correlation model development, and finally model evaluation. ABSTRACT RESEARCH ARTICLE KEYWORDS European Journal of Chemistry ISSN 2153-2249 (Print) / ISSN 2153-2257 (Online) – Copyright © 2018 The Authors – Atlanta Publishing House LLC – Printed in the USA. This work is published and licensed by Atlanta Publishing House LLC – CC BY NC – Some Rights Reserved. http://dx.doi.org/10.5155/eurjchem.9.3.275-280.1765 http://dx.doi.org/10.5155/eurjchem.9.3.275-280.1765 https://crossmark.crossref.org/dialog/?doi=10.5155/eurjchem.9.3.275-280.1765&domain=pdf&date_stamp=2018-09-30 http://www.eurjchem.com/ http://dx.doi.org/10.5155/eurjchem.9.3.275-280.1765 mailto:maysoon256@hotmail.com mailto:asmsaeed@gmail.com mailto:nhmmohamed@yahoo.com mailto:maysoon256@hotmail.com http://www.eurjchem.com/ https://crossmark.crossref.org/dialog/?doi=10.5155/eurjchem.9.3.275-280.1765&domain=pdf&date_stamp=2018-09-30� 276 Almahdi et al. / European Journal of Chemistry 9 (3) (2018) 275-280 Table 1. Biological activities and structures of α,β-unsaturated carbonyl compounds reported by Syam [9]. O R2 R1 R5 R4 R3 Compound R1 R2 R3 R4 R5 IC50 Pc 1 H H H H H 6.875 5.16 2 CH3 H H H H 13.62 4.87 3 OCH3 H H H H 19.15 4.72 4 H H H H Cl 7.992 5.10 5 OH H H H Cl 10.01 5.00 6 H H H CH3 H 9.34 5.03 7 OH H H CH3 H 8.343 5.08 8 CH3 H H CH3 H 10.16 4.99 9 OCH3 H H CH3 H 9.53 5.02 10 H H OCH3 H H 11.62 4.93 11 CH3 H OCH3 H H 32.37 4.49 12 OCH3 H OCH3 H H 41.44 4.38 13 H H N(CH3)2 H H 99.29 4.00 14 CH3 H N(CH3)2 H H 57.28 4.24 15 H H H H OH 9.353 5.03 16 H CH3 H H OH 6.873 5.16 17 H OCH3 H H OH 7.149 5.15 18 H H H Cl H 5.251 5.28 Table 2. Values of molecular descriptors calculated for training set. ID FW MV ST MR E RI D Log P (o/w) 1 208.255 189.8 44.7 67.10 53.88202 1.624 1.097 4.065 2 222.281 206.0 43.2 71.93 56.08740 1.615 1.078 4.363 4 242.700 201.7 46.4 72.00 61.17518 1.632 1.202 4.655 5 238.281 204.5 49.9 73.81 55.46881 1.641 1.165 4.053 6 222.281 206.0 43.2 71.93 56.10360 1.615 1.078 4.400 7 238.281 204.5 49.9 73.81 53.37540 1.641 1.165 4.092 8 236.308 222.3 42.0 76.75 58.28762 1.606 1.062 4.698 9 252.307 230.1 41.8 78.61 65.41583 1.598 1.096 4.356 11 252.307 230.1 41.8 78.61 68.13621 1.598 1.096 4.319 12 268.307 237.8 41.6 80.46 72.26687 1.591 1.128 3.977 13 251.322 227.8 45.9 81.42 72.06151 1.633 1.103 3.980 14 265.349 244.0 44.5 86.24 71.59818 1.624 1.087 4.278 15 224.254 188.2 52.1 68.99 56.14378 1.653 1.191 3.755 16 238.281 204.5 49.9 73.81 58.24109 1.641 1.165 4.090 18 242.700 201.7 46.4 72.00 55.27224 1.632 1.202 4.694 QSAR studies have predictive ability and simultaneously provide deeper insight into mechanism of drug receptor interactions [8]. In the present work, quantitative structure activity relationship study was performed to obtain a QSAR model, which was used to predict the biological activity against human breast adenocarcinoma cell line MCF-7 of a set of designed α,β unsaturated carbonyl compounds (A1-A16). In addition, docking studies was carried out to understand binding interaction between designed compounds (A1-A16) and selected protein 3dkc. 2. Experimental 2.1. QSAR studies A total of eighteen α,β-unsaturated carbonyl compounds reported by Syam [9] was used in QSAR study. They synthesized several α,β-unsaturated compounds and reported their in vitro cytotoxicity against various human cell lines, including human breast adenocarcinoma cell line MCF-7. ChemBioDraw Ultra v14.0.0117 software (Copyright 1998- 2014 CambridgeSoft Corporation, a subsidiary of Perkin Elmer, Inc.) was used for drawing series of the studied compounds. The experimental IC50, concentration of the compounds (IC50 in microgram per milliliter) exhibiting 50% inhibition of cell growth for human breast cancer (MCF-7) was converted to negative logarithmic concentration in moles (Pc), values along with the α,β-unsaturated carbonyl structures can be found on Table 1. The QSAR study was performed using molecular modeling studies of compounds with Molecular Operating Environment software package (MOE, v2010.10; Chemical Computing Group Inc.). A set of 15 compounds was used as a training set for the present QSAR modeling, the three remaining α,β-unsaturated carbonyl compounds chosen randomly were used as an external test set to assess the predictive power of the resulting QSAR model. 2.1.1. Molecular descriptors Molecular descriptors (physicochemical properties) are chemical information that is encoded within the molecular structures for which numerous sets of algorithms are available for such transformation [7]. Total of eight molecular descript- tors namely, formula weight (FW), molar volume (MV), surface tension (ST), molar reactivity (MR), potential energy (E), refractive index (RI), density (D) and Log P (octanol/water) (Log P(o/w)) were calculated for each compound in training set using MOE and ACD/lab (Copyright 1994-2016, ACD/Labs 2016.2, File Version C30E41, Build 90752, 20 Dec 2016) programmes and listed in Table 2. To select the optimal subset of physicochemical proper- ties, highly correlated chemical descriptors were excluded through covariance analysis using a correlation matrix. The ratio of the number of compounds to the physico- chemical descriptors used for the correlation is usually 5:1 [10]. 2018 – European Journal of Chemistry – CC BY NC – DOI: 10.5155/eurjchem.9.3.275-280.1765 Almahdi et al. / European Journal of Chemistry 9 (3) (2018) 275-280 277 Table 3. Statistical parameters used for statistical quality of model. r r2 RMSE Q2 s F P value 0.9202 0.84684 0.14550 0.7621 0.38484 101.434 0.010 Table 4. Observed and predicted Pc for training set and cross validation against human breast cancer (MCF-7). ID Pcobs. Pcpred. Residuals CVpred. Residuals 1 5.16 5.051935 0.108065 5.011246 0.148754 2 4.87 5.019001 -0.149000 5.060885 -0.190880 4 5.10 5.087039 0.012961 5.075714 0.024286 5 5.00 5.087764 -0.087760 5.105386 -0.105390 6 5.03 5.029799 0.000202 5.029741 0.000259 7 5.08 5.190366 -0.110370 5.216456 -0.136460 8 4.99 5.002559 -0.012560 5.009727 -0.019730 9 5.02 4.642254 0.377746 4.589873 0.430127 11 4.49 4.513175 -0.023180 4.517511 -0.027510 12 4.38 4.279251 0.100750 4.234203 0.145797 13 4.00 4.249300 -0.249300 4.351688 -0.351690 14 4.24 4.336486 -0.096490 4.367906 -0.127910 15 5.03 5.007334 0.022666 4.992760 0.037240 16 5.16 4.979438 0.180562 4.952883 0.207117 18 5.28 5.354298 -0.074300 5.415618 -0.135620 Table 5. Predicted Pc values of test set. ID Pcobs. Pcpred. Residuals 3 4.72 4.66 0.06 10 4.93 4.67 0.26 17 5.15 4.69 0.46 Thus, for the 15 compounds in the training set, only three physicochemical descriptors (D, E, Log P (o/w)) were correla- ted simultaneously with their breast cancer activity. Lastly, a QSAR model equation was derived by using MLR statistical method (Equation 1). The statistical quality of the regression equations were justified by statistical parameters such as the root mean square error (RMSE), correlation coefficient (r), squared correlation coefficient (r2), standard error of estimate (s) and F-test value (ratio between the variances of observed and calculated activities, F). Calculation of statistical para- meters was carried out by using statistical program SPSS version 15.5. Pc = 4.37301 + 1.59035 × D + 0.31076 … … × log P (o/w) - 0.04322 × E (1) In addition, the predictive powers of the equations were validated by the leave-one-out (LOO) cross-validation method (CV). The cross validated squared correlation coefficient (Q2) was considered for the validation of this model (Table 3). The developed QSAR model equation showed a relation- ship between predicted Pc values (-Log IC50) and correlated three chemical descriptors. It is evident from the model equation that the molecular descriptors, namely density and Log (o/w) partition coefficient, are positively correlated. On the other hand, potential energy showed negative correlation. 2.1.2. Validation of quantitative structure-activity relationship model To test the internal stability and predictive ability of the derived QSAR model, it was validated or cross-checked by the internal validation and external validation test procedure as follows: (a) Internal validation by training set compounds: This step was carried out by using LOO validation method. For calculating cross validation regression coefficient (Q2), each molecule in the training set was eliminated once, and the activity of the eliminated molecule was predicted by using the QSAR model developed by the remaining molecules. (b) External validation by test set compounds: In this step the activity of each molecule in the test set was predicted by using the derived QSAR model developed by the training set compounds [11]. The observed activities and those provided by QSAR studies (Equation 1) for training and test set were presented in Tables 4 and 5. It should be noted that the predicated anticancer activities by obtained QSAR model was close to those experimentally observed, indicating that these model can be safely applied for predication of more effective hits having the same skeletal framework as that of the potent anticancer compound. 2.1.3. Predict the activity of designed α,β-unsaturated carbonyl compounds (A1-16) A set of designed α,β-unsaturated carbonyl compounds (A1-16) were sketched using the computer software ChemBioDraw program. These compounds were not used in the developed QSAR model, but sketched to predict their anticancer activity against MCF-7 by using developed QSAR model (Equation 1). The predicted activity along with the sketched structures was listed in Table 6. 2.2. Molecular docking Molecular docking study is a well-established technique to determine the interactions between two molecules (ligand- receptor) and to find the best orientation of ligand that would form a complex with overall minimum energy [12]. It was of interest to perform molecular docking for the designed set (A1-16), in order to understand the ligand–protein interaction in detail. The protein file (PDB ID: 3dkc) selected for this purpose obtained from Protein Data Bank and further optimi- zed and minimized to obtain a low energy and structural correct target protein. 3. Results and discussion 3.1. QSAR studies Quantitative structure activity relationships studies are useful tools in the rational search for bioactive molecules. The main success of the QSAR method is the possibility to estimate the characteristics of new chemical compounds without the need to synthesize and test them [8]. 2018 – European Journal of Chemistry – CC BY NC – DOI: 10.5155/eurjchem.9.3.275-280.1765 278 Almahdi et al. / European Journal of Chemistry 9 (3) (2018) 275-280 Table 6. Structures of designed α-β unsaturated carbonyl compounds with their predicted Pc values against human breast cancer (MCF-7). R O R1 ID R R1 Predicted Pc ID R R1 Predicted Pc A1 Cl -H 5.42 A9 -NO2 4.47 A2 -H 5.13 A10 Cl -NO2 4.82 A3 O -H 5.12 A11 N H3C H3C -NO2 3.74 A4 -Br 5.75 A12 -NO2 4.52 A5 Cl -Br 6.08 A13 O -NO2 4.94 A6 N H3C H3C -Br 4.99 A14 Cl -CH3 5.38 A7 -Br 5.77 A15 -CH3 5.09 A8 O -Br 6.24 A16 O -CH3 5.47 y= 0.8468x + 0.7437 r= 0.9202, r² = 0.8468 4.0 4.2 4.4 4.6 4.8 5.0 5.2 5.4 3.9 4.1 4.3 4.5 4.7 4.9 5.1 5.3 Pr ed ic te d Pc Observed Pc Figure 1. Predicted versus observed Pc values of training set against human breast cancer MCF-7. y= 0.8056x+ 0.9506 r= 0.8730, r² = 0.7621 4.0 4.2 4.4 4.6 4.8 5.0 5.2 5.4 3.9 4.1 4.3 4.5 4.7 4.9 5.1 5.3 Pr ed ic te d Pc Observed Pc Figure 2. Predicted versus observed Pc values of cross validation against human breast cancer MCF-7. The resulted QSAR model equation showed a high square of the correlation coefficient (r2 = 0.84684) and low root mean square error (RMSE = 0.1455), all other statistical parameters calculated to justified the statistical quality of model were in acceptable rang (Table 3). The predictive ability of QSAR model was validated through leave one out cross validation (Q2 = 0.7621) and external validation methods. Figures 1, 2 and 3 show the corresponding scatter plots of the observed versus predicted Pc values for the training set, cross validation and test set compounds against breast cancer cell line, respectively. 2018 – European Journal of Chemistry – CC BY NC – DOI: 10.5155/eurjchem.9.3.275-280.1765 Almahdi et al. / European Journal of Chemistry 9 (3) (2018) 275-280 279 y = 0.0744x + 4.3113 r= 0.9711, r² = 0.943 4.66 4.67 4.68 4.69 4.70 4.7 4.8 4.9 5.0 5.1 5.2 Pr ed ic te d Pc Observed Pc Figure 3. Predicted versus observed Pc values of test set against human breast cancer MCF-7. Figure 4. 2D molecular docking model of compound A6 with 3dkc. Tables 4 and 5 summarize the predicted Pc values of the training set (r2= 0.8468) and test set (r2 = 0.9430). QSAR model obtained was used to predict the biological activity of a set of designed α-β unsaturated carbonyl compounds (A1-16) against human breast cancer (MCF-7) Table 6. It is noteworthy that model equation was derived using the entire data set of compounds (n = 15) and no outliers were identified. Figure 5. 3D model of the interaction between compound A6 and 3dkc binding site. 3.2. Docking study A number of human malignancies exhibit sustained stimulation, mutation or gene amplification of the receptor tyrosine kinase human mesenchymal-epithelial transition factor (c-Met) [13]. The overexpression and mutations of c- Met and its natural ligand, hepatocyte growth factor (HGF), have been found in different types of tumors including breast, ovarian, colorectal, lung, gastric, pancreatic, melanoma, prostate, head and neck, glioma, hepatocellular, renal and a number of sarcomas. The 3dkc is the crystal structure of c-Met kinase in complex with ATP which is widely used [14]. Performed molecular docking for all designed α,β- unsaturated carbonyl compounds (A1-16) on the active site of 3dkc may provide an understanding of their effect as antitumor agents. All docking procedures were achieved by MOE (Molecular Operating Environment) software 2010.10. Molecular docking suggested that all of the designed α,β- unsaturated carbonyl compounds (A1-16) were capable of forming a metal complex with the Mg ion through Asp1222 (bond length 2.10 Å) and Asn1209 (bond length 2.03 Å). Compound A6 showed three interactions, beside two interactions mentioned above, one π-cation interaction was revealed between Arg1208 residue with phenyl ring of 4- bromo benzoyl moiety Figures 4 and 5. Docking score of designed derivatives A1-16 ranging between -22.9170 to -16.1714 kcal/mol. The energy score (S) of the complexes between each designed compounds (A1-16) and the active site of the receptor as well as amino acid interacted are listed in Table 7. 4. Conclusion From the QSAR studies, it was noticed that the Pc values has been increase in compounds A4-A8 which contain –Br group, compared to the corresponding non-brominated compounds. Compounds A9-A13 which contain –NO2 group showed low Pc values which indicate less activity toward human breast cancer cell line MCF-7. The molecular docking study of A1-A16 compounds has been done for the better understanding of the ligand-receptor interaction and the results confirmed that these compounds were a potential inhibitor of c-Met kinase. In the view of this study, further research can be carried out on designed compounds (A4-A8) to investigate there in vitro anticancer activity against breast cancer. 2018 – European Journal of Chemistry – CC BY NC – DOI: 10.5155/eurjchem.9.3.275-280.1765 280 Almahdi et al. / European Journal of Chemistry 9 (3) (2018) 275-280 Table 7. Binding scores and amino acid interactions of the docked designed carbonyl compounds (A1-16) on the active site of 3dkc. ID Molecular weight S (Kcal/mol) Amino acid interaction Type of interaction Length (Å) A1 242.70 -21.356 Asp1222 Asn1209 Metal complex (Mg) Metal complex (Mg) 2.10 2.03 A2 234.29 -21.1818 Asp1222 Asn1209 Metal complex (Mg) Metal complex (Mg) 2.10 2.03 A3 198.21 -16.1714 Asp1222 Asn1209 Metal complex (Mg) Metal complex (Mg) 2.10 2.03 A4 287.15 -22.9170 Asp1222 Asn1209 Metal complex (Mg) Metal complex (Mg) 2.10 2.03 A5 321.59 -20.5912 Asp1222 Asn1209 Metal complex (Mg) Metal complex (Mg) 2.10 2.03 A6 330.21 -22.5738 Asp1222 Asn1209 Arg1208 Metal complex (Mg) Metal complex (Mg) π-cation interaction 2.10 2.03 - A7 313.18 -20.6469 Asp1222 Asn1209 Metal complex (Mg) Metal complex (Mg) 2.10 2.03 A8 277.11 -22.0121 Asp1222 Asn1209 Metal complex (Mg) Metal complex (Mg) 2.10 2.03 A9 253.25 -23.2468 Asp1222 Asn1209 Metal complex (Mg) Metal complex (Mg) 2.10 2.03 A10 287.69 -20.7988 Asp1222 Asn1209 Metal complex (Mg) Metal complex (Mg) 2.10 2.03 A11 296.32 -23.4193 Asp1222 Asn1209 Metal complex (Mg) Metal complex (Mg) 2.10 2.03 A12 279.29 -22.4421 Asp1222 Asn1209 Metal complex (Mg) Metal complex (Mg) 2.10 2.03 A13 243.21 -21.7925 Asp1222 Asn1209 Metal complex (Mg) Metal complex (Mg) 2.10 2.03 A14 256.72 -20.3991 Asp1222 Asn1209 Metal complex (Mg) Metal complex (Mg) 2.10 2.03 A15 248.31 -20.5758 Asp1222 Asn1209 Metal complex (Mg) Metal complex (Mg) 2.10 2.03 A16 212.24 -22.5230 Asp1222 Asn1209 Metal complex (Mg) Metal complex (Mg) 2.10 2.03 Disclosure statement Conflict of interests: The authors declare that they have no conflict of interest. Author contributions: All authors contributed equally to this work. Ethical approval: All ethical guidelines have been adhered. ORCID Maysoon Mohammed Almahdi http://orcid.org/0000-0002-9970-5662 Ahmed Elsadig Mohamed Saeed http://orcid.org/0000-0003-2665-3803 Nadia Hanafy Metwally http://orcid.org/0000-0002-3591-7148 References [1]. Rathore, P.; Yaseen, S.; Ovais, S.; Bashir, R.; Yaseen, R.; Hameed, A. D.; Samima, M.; Gupta, R.; Hussain, F.; Javed, K. Bioorg. Med. Chem. Lett. 2014, 24, 1685-1691. [2]. Karabacak, M.; Altıntop, M. D.; Ciftci, H. I.; Koga, R.; Otsuka, M.; Fujita, M.; Ozdemir, A. Molecules 2015, 20, 19066-19084. [3]. Ghorab, M. M.; Ceruso, M.; Alsaid, M. S.; Nissan, Y. M.; Arafa, R. K.; Supuran, C. T. Eur. J. Med. Chem. 2014, 87, 186-196. [4]. Ferdous, S.; Mirza, M. U.; Saeed, U. Int. J. Comput. Appl. 2013, 67, 0975-8887. [5]. Farag, A. M.; Mayhoub, A. S.; Eldebss, T. M. A.; Amr, A. E.; Ali, K. A. K.; Abdel-Hafez, N. A.; AbdullaAhmad, M. M. Arch. Pharm. Chem. 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This work is published and licensed by Atlanta Publishing House LLC, Atlanta, GA, USA. The full terms of this license are available at http://www.eurjchem.com/index.php/eurjchem/pages/view/terms and incorporate the Creative Commons Attribution-Non Commercial (CC BY NC) (International, v4.0) License (http://creativecommons.org/licenses/by-nc/4.0). By accessing the work, you hereby accept the Terms. This is an open access article distributed under the terms and conditions of the CC BY NC License, which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited without any further permission from Atlanta Publishing House LLC (European Journal of Chemistry). No use, distribution or reproduction is permitted which does not comply with these terms. Permissions for commercial use of this work beyond the scope of the License (http://www.eurjchem.com/index.php/eurjchem/pages/view/terms) are administered by Atlanta Publishing House LLC (European Journal of Chemistry). 2018 – European Journal of Chemistry – CC BY NC – DOI: 10.5155/eurjchem.9.3.275-280.1765 http://orcid.org/0000-0002-9970-5662 http://orcid.org/0000-0003-2665-3803 http://orcid.org/0000-0002-3591-7148 http://www.eurjchem.com/index.php/eurjchem/pages/view/terms http://creativecommons.org/licenses/by-nc/4.0 http://www.eurjchem.com/index.php/eurjchem/pages/view/terms 1. Introduction 2. Experimental 2.1. QSAR studies 2.1.1. Molecular descriptors 2.1.2. Validation of quantitative structure-activity relationship model 2.1.3. Predict the activity of designed α,β-unsaturated carbonyl compounds (A1-16) 2.2. Molecular docking 3. Results and discussion 3.1. QSAR studies 3.2. Docking study 4. Conclusion Disclosure statement ORCID References PrintField10: PrintField11: PrintField12: PrintField13: PrintField14: PrintField15: PrintField20: PrintField21: PrintField22: PrintField23: PrintField24: PrintField25: