Computational identification of erα antagonists derived from natural products for breast cancer treatment
DOI:
https://doi.org/10.56294/saludcyt20262681Keywords:
Breast cancer, Estrogen receptor α, Natural compounds, Molecular docking, ADMET, MDAbstract
Estrogen receptor alpha-positive (ERα+) breast cancer remains the most prevalent hormone-driven malignancy in women. While current endocrine therapies target ERα, emerging drug resistance underscores the need for novel antagonists. This study computationally evaluates natural compounds from the ZINC database as potential ERα antagonists using multi-stage in silico approaches. Molecular docking (HTVS, SP, XP) identified two compounds, ZINC000085627072 and ZINC000085592636, with superior binding affinities (XP scores: -14.811 and -14.366 kcal/mol) compared to the reference antagonist H3B-9224 (-13.620 kcal/mol). MM-GBSA binding free energy calculations further corroborated their stability, yielding energies of -61.51, -88.77, and -85.38 kcal/mol for ZINC000085627072, ZINC000085592636, and H3B-9224, respectively. Pharmacokinetic profiling via ADME analysis revealed acceptable properties for both natural compounds. Molecular dynamics (MD) simulations over 100 ns demonstrated stable binding: ZINC000085592636 and H3B-9224 exhibited comparable RMSD trajectories (~3 Å), while ZINC000085627072 showed moderate fluctuations (~4 Å). Protein-ligand flexibility analysis (RMSF) revealed average ligand-RMSF values of 1.4 ±1.14 Å (ZINC000085627072), 1.2 ±0.4 Å (ZINC000085592636), and 1.4 ±1.1 Å (H3B-9224), with protein-RMSF consistently at ~3 Å, indicating minimal structural fluctuations. These results suggest ZINC000085627072 and ZINC000085592636 as promising ERα antagonists with superior predicted affinity to H3B-9224, warranting further experimental validation. This integrated computational framework highlights the potential of natural product-derived scaffolds in addressing ERα+ breast cancer drug resistance.
References
[1] V.H. Masand, S.A. Al-Hussain, A.Y. Alzahrani, A.A. Al-Mutairi, R.A. Hussien, A. Samad, M.E.A. Zaki, Estrogen Receptor Alpha Binders for Hormone-Dependent Forms of Breast Cancer: e-QSAR and Molecular Docking Supported by X-ray Resolved Structures, ACS Omega 9 (2024) 16759–16774. https://doi.org/10.1021/acsomega.4c00906. DOI: https://doi.org/10.1021/acsomega.4c00906
[2] B.J. Warude, S.N. Wagh, V.A. Chatpalliwar, M. Yildirim, I. Celik, M. Rudrapal, J. Khan, S. Chinnam, A.A. Garud, V.S. Neharkar, Design, docking, MD simulation and in-silco ADMET prediction studies of novel indolebased benzamides targeting estrogen receptor alfa positive for effective breast cancer therapy, Pharmacia 70 (2023) 307–316. https://doi.org/10.3897/pharmacia.70.e100356. DOI: https://doi.org/10.3897/pharmacia.70.e100356
[3] L.A. Morelos-Garnica, S. Guzmán-Velázquez, I.I. Padilla-Martínez, J.R. García-Sánchez, M. Bello, N. Bakalara, D. Méndez-Luna, J. Correa-Basurto, In silico design and cell-based evaluation of two dual anti breast cancer compounds targeting Bcl-2 and GPER, Sci. Rep. 13 (2023) 1–14. https://doi.org/10.1038/s41598-023-43860-x. DOI: https://doi.org/10.1038/s41598-023-43860-x
[4] H. Cao, Y. Sun, L. Wang, Y. Pan, Z. Li, Y. Liang, In silico identification of novel inhibitors targeting the DNA-binding domain of the human estrogen receptor alpha, J. Steroid Biochem. Mol. Biol. 213 (2021) 1–9. https://doi.org/10.1016/j.jsbmb.2021.105966. DOI: https://doi.org/10.1016/j.jsbmb.2021.105966
[5] A. Alamri, A. Rauf, A.A. Khalil, A. Alghamdi, A. Alafnan, A. Alshammari, F. Alshammari, J.A. Malik, S. Anwar, In Silico Screening of Marine Compounds as an Emerging and Promising Approach against Estrogen Receptor Alpha-Positive Breast Cancer, Biomed Res. Int. (2021) 1–7. https://doi.org/10.1155/2021/9734279.
[6] J.S. Disch, J.M. Duffy, E.C.Y. Lee, D. Gikunju, B. Chan, B. Levin, M.I. Monteiro, S.A. Talcott, A.C. Lau, F. Zhou, A. Kozhushnyan, N.E. Westlund, P.B. Mullins, Y. Yu, M. Von Rechenberg, J. Zhang, Y.A. Arnautova, Y. Liu, Y. Zhang, A.J. McRiner, A.D. Keefe, A. Kohlmann, M.A. Clark, J.W. Cuozzo, C. Huguet, S. Arora, Bispecific Estrogen Receptor α Degraders Incorporating Novel Binders Identified Using DNA-Encoded Chemical Library Screening, J. Med. Chem. 64 (2021) 5049–5066. https://doi.org/10.1021/acs.jmedchem.1c00127. DOI: https://doi.org/10.1021/acs.jmedchem.1c00127
[7] F. Lumachi, A. Brunello, M. Maruzzo, U. Basso, S. Basso, Treatment of Estrogen Receptor-Positive Breast Cancer, Curr. Med. Chem. 20 (2013) 596–604. https://doi.org/10.2174/092986713804999303. DOI: https://doi.org/10.2174/092986713804999303
[8] D. Bafna, F. Ban, P.S. Rennie, K. Singh, A. Cherkasov, Computer-aided ligand discovery for estrogen receptor alpha, Int. J. Mol. Sci. 21 (2020) 1–49. https://doi.org/10.3390/ijms21124193. DOI: https://doi.org/10.3390/ijms21124193
[9] C. Panis, L. Pizzatti, A.C. Herrera, S. Corrêa, R. Binato, E. Abdelhay, Label-free proteomic analysis of breast cancer molecular subtypes, J. Proteome Res. 13 (2014) 4752–4772. https://doi.org/10.1021/pr500676x. DOI: https://doi.org/10.1021/pr500676x
[10] F.S. Asif Bilal, Fouzia Tanvir, Sibtain Ahmad, Riaz Mustafa, Ghanwa Fatima, In-silico drug discovery from phytoactive compounds against estrogen receptor beta (ERβ) inducing human mammary carcinoma, Sci. Partn. J. (2024) 1–17.
[11] Y. Liu, H. Ma, J. Yao, ERα, a key target for cancer therapy: A review, Onco. Targets. Ther. 13 (2020) 2183–2191. https://doi.org/10.2147/OTT.S236532. DOI: https://doi.org/10.2147/OTT.S236532
[12] P. Bansode, R. Anantacharya, M. Dhanavade, S. Kamble, S. Barale, K. Sonawane, N.D. Satyanarayan, G. Rashinkar, Evaluation of drug candidature: In silico ADMET, binding interactions with CDK7 and normal cell line studies of potentially anti-breast cancer enamidines, Comput. Biol. Chem. 83 (2019) 107124. https://doi.org/10.1016/j.compbiolchem.2019.107124. DOI: https://doi.org/10.1016/j.compbiolchem.2019.107124
[13] A.P. Widiyana, T. Widiandani, S. Siswodihardjo, 5-O-Acetylpinostrobin derivatives inhibit estrogen alpha and progesterone receptors through a molecular docking approach, Pharm. Educ. 24 (2024) 244–250. https://doi.org/10.46542/pe.2024.243.244250. DOI: https://doi.org/10.46542/pe.2024.243.244250
[14] J.J. Sahayarayan, K.S. Rajan, R. Vidhyavathi, M. Nachiappan, D. Prabhu, S. Alfarraj, S. Arokiyaraj, A.N. Daniel, In-silico protein-ligand docking studies against the estrogen protein of breast cancer using pharmacophore based virtual screening approaches, Saudi J. Biol. Sci. 28 (2021) 400–407. https://doi.org/10.1016/j.sjbs.2020.10.023. DOI: https://doi.org/10.1016/j.sjbs.2020.10.023
[15] N.M. O’Boyle, I. Barrett, L.M. Greene, M. Carr, D. Fayne, B. Twamley, A.J.S. Knox, N.O. Keely, D.M. Zisterer, M.J. Meegan, Lead Optimization of Benzoxepin-Type Selective Estrogen Receptor (ER) Modulators and Downregulators with Subtype-Specific ERα and ERβ Activity, J. Med. Chem. 61 (2018) 514–534. https://doi.org/10.1021/acs.jmedchem.6b01917. DOI: https://doi.org/10.1021/acs.jmedchem.6b01917
[16] N. Thongon, N. Boonmuen, K. Suksen, P. Wichit, A. Chairoungdua, P. Tuchinda, A. Suksamrarn, W. Winuthayanon, P. Piyachaturawat, Selective Estrogen Receptor Modulator (SERM)-like Activities of Diarylheptanoid, a Phytoestrogen from Curcuma comosa, in Breast Cancer Cells, Pre-osteoblast Cells, and Rat Uterine Tissues, J. Agric. Food Chem. 65 (2017) 3490–3496. https://doi.org/10.1021/acs.jafc.7b00769. DOI: https://doi.org/10.1021/acs.jafc.7b00769
[17] T. De Marchi, E. Kuhn, L.J. Dekker, C. Stingl, R.B.H. Braakman, M. Opdam, S.C. Linn, F.C.G.J. Sweep, P.N. Span, T.M. Luider, J.A. Foekens, J.W.M. Martens, S.A. Carr, A. Umar, Targeted MS Assay Predicting Tamoxifen Resistance in Estrogen-Receptor-Positive Breast Cancer Tissues and Sera, J. Proteome Res. 15 (2016) 1230–1242. https://doi.org/10.1021/acs.jproteome.5b01119. DOI: https://doi.org/10.1021/acs.jproteome.5b01119
[18] A.S. Lu, M. Rouhimoghadam, C.K. Arnatt, E.J. Filardo, A.K. Salem, Proteolytic Targeting Chimeras with Specificity for Plasma Membrane and Intracellular Estrogen Receptors, Mol. Pharm. 18 (2021) 1455–1469. https://doi.org/10.1021/acs.molpharmaceut.1c00018. DOI: https://doi.org/10.1021/acs.molpharmaceut.1c00018
[19] O. Nosseir, Y. Syam, A. Hashim, R. El-Haggar, M. Anwar, W. Zaghary, In Silico ADME Prediction and Molecular Docking of 1,2,3-Triazole-based Compounds Against Human Aromatase Cytochrome P450, J. Adv. Pharm. Res. 7 (2023) 232–242. https://doi.org/10.21608/aprh.2023.225722.1231. DOI: https://doi.org/10.21608/aprh.2023.225722.1231
[20] A.M. Alamri, F.A. Alkhilaiwi, N.U. Khan, R.M. Mashat, M. Tasleem, Exploring pathogenic SNPs and estrogen receptor alpha interactions in breast cancer: An in silico approach, Heliyon 10 (2024) e37297. https://doi.org/10.1016/j.heliyon.2024.e37297. DOI: https://doi.org/10.1016/j.heliyon.2024.e37297
[21] S. Chakraborty, S. Cole, N. Rader, C. King, R. Rajnarayanan, P.K. Biswas, In silico design of peptidic inhibitors targeting estrogen receptor alpha dimer interface, Mol. Divers. 16 (2012) 441–451. https://doi.org/10.1007/s11030-012-9378-x. DOI: https://doi.org/10.1007/s11030-012-9378-x
[22] M. Pavlin, A. Spinello, M. Pennati, N. Zaffaroni, S. Gobbi, A. Bisi, G. Colombo, A. Magistrato, A Computational Assay of Estrogen Receptor α Antagonists Reveals the Key Common Structural Traits of Drugs Effectively Fighting Refractory Breast Cancers, Sci. Rep. 8 (2018) 1–11. https://doi.org/10.1038/s41598-017-17364-4. DOI: https://doi.org/10.1038/s41598-017-17364-4
[23] S.S. Ashtekar, N.M. Bhatia, M.S. Bhatia, Development of leads targeting ER-α in breast cancer: An in silico exploration from natural domain, Steroids 131 (2018) 14–22. https://doi.org/10.1016/j.steroids.2017.12.016. DOI: https://doi.org/10.1016/j.steroids.2017.12.016
[24] K. Rajagopal, A. Kalusalingam, A.R. Bharathidasan, A. Sivaprakash, K. Shanmugam, M. Sundaramoorthy, G. Byran, In Silico Drug Design of Anti-Breast Cancer Agents, Molecules 28 (2023) 1–27. https://doi.org/10.3390/molecules28104175. DOI: https://doi.org/10.3390/molecules28104175
[25] S.H. Abdullahi, A. Uzairu, G.A. Shallangwa, S. Uba, A.B. Umar, Computational modeling, ligand-based drug design, drug-likeness and ADMET properties studies of series of chromen-2-ones analogues as anti-cancer agents, Bull. Natl. Res. Cent. 46 (2022) 1–25. https://doi.org/10.1186/s42269-022-00869-y. DOI: https://doi.org/10.1186/s42269-022-00869-y
[26] K.D. Flach, W. Zwart, The first decade of estrogen receptor cistromics in breast cancer, J. Endocrinol. 229 (2016) R43–R56. https://doi.org/10.1530/JOE-16-0003. DOI: https://doi.org/10.1530/JOE-16-0003
[27] M. Ervina, M. Pratama, H. Poerwono, J. Ekowati, R. Widyowati, K. Matsunami, Sukardiman, In silico estrogen receptor alpha antagonist studies and toxicity prediction of Melia azedarach leaves bioactive ethyl acetate fraction, J. Adv. Pharm. Technol. Res. 12 (2021) 236–241. https://doi.org/10.4103/japtr.JAPTR_198_21. DOI: https://doi.org/10.4103/japtr.JAPTR_198_21
[28] O. Iwaloye, P.O. Ottu, F. Olawale, O.O. Babalola, O.O. Elekofehinti, B. Kikiowo, A.E. Adegboyega, H.N. Ogbonna, C.F. Adeboboye, I.M. Folorunso, A.E. Fakayode, M.O. Akinjiyan, S.A. Onikanni, S. Shityakov, Computer-aided drug design in anti-cancer drug discovery: What have we learnt and what is the way forward?, Informatics Med. Unlocked 41 (2023) 1–20. https://doi.org/10.1016/j.imu.2023.101332. DOI: https://doi.org/10.1016/j.imu.2023.101332
[29] S.P. Leelananda, S. Lindert, Computational methods in drug discovery, Beilstein J. Org. Chem. 12 (2016) 2694–2718. https://doi.org/10.3762/bjoc.12.267. DOI: https://doi.org/10.3762/bjoc.12.267
[30] D. Prada-Gracia, S. Huerta-Yépez, L.M. Moreno-Vargas, Application of computational methods for anticancer drug discovery, design, and optimization, Boletín Médico Del Hosp. Infant. México (English Ed. 73 (2016) 411–423. https://doi.org/10.1016/j.bmhime.2017.11.040. DOI: https://doi.org/10.1016/j.bmhime.2017.11.040
[31] A. V. Sadybekov, V. Katritch, Computational approaches streamlining drug discovery, Nature 616 (2023) 673–685. https://doi.org/10.1038/s41586-023-05905-z. DOI: https://doi.org/10.1038/s41586-023-05905-z
[32] J.D. Romano, N.P. Tatonetti, Informatics and computational methods in natural product drug discovery: A review and perspectives, Front. Genet. 10 (2019) 1–16. https://doi.org/10.3389/fgene.2019.00368. DOI: https://doi.org/10.3389/fgene.2019.00368
[33] G. Sliwoski, S. Kothiwale, J. Meiler, E.W. Lowe, Computational methods in drug discovery, Pharmacol. Rev. 66 (2014) 334–395. https://doi.org/10.1124/pr.112.007336. DOI: https://doi.org/10.1124/pr.112.007336
[34] C. Cerchia, J.C. Basurto, A. Lupo, A. Lavecchia, Applications of Generative AI, Front. Drug Discov. (2024) 1–2. https://doi.org/10.1007/978-3-031-46238-2. DOI: https://doi.org/10.1007/978-3-031-46238-2
[35] O.N. Koborova, D.A. Filimonov, A. V. Zakharov, A.A. Lagunin, S.M. Ivanov, A. Kel, V. V. Poroikov, In silico method for identification of promising anticancer drug targets, SAR QSAR Environ. Res. 20 (2009) 755–766. https://doi.org/10.1080/10629360903438628. DOI: https://doi.org/10.1080/10629360903438628
[36] D. Prada-gracia, S. Huerta-yépez, L.M. Moreno-vargas, Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID- 19 . The COVID-19 resource centre is hosted on Elsevier Connect , the company ’ s public news and information, Bol Med Hosp Infant Mex 73 (2020) 411–423.
[37] W. Cui, A. Aouidate, S. Wang, Q. Yu, Y. Li, S. Yuan, Discovering Anti-Cancer Drugs via Computational Methods, Front. Pharmacol. 11 (2020) 1–14. https://doi.org/10.3389/fphar.2020.00733. DOI: https://doi.org/10.3389/fphar.2020.00733
[38] S. Brogi, T.C. Ramalho, K. Kuca, J.L. Medina-Franco, M. Valko, Editorial: In silico Methods for Drug Design and Discovery, Front. Chem. 8 (2020) 1–5. https://doi.org/10.3389/fchem.2020.00612. DOI: https://doi.org/10.3389/fchem.2020.00612
[39] A.A. Alzain, F.A. Elbadwi, T.H. Shoaib, A.E. Sherif, W. Osman, A. Ashour, G.A. Mohamed, S.R.M. Ibrahim, E.J. Roh, A.H.E. Hassan, Integrating computational methods guided the discovery of phytochemicals as potential Pin1 inhibitors for cancer: pharmacophore modeling, molecular docking, MM-GBSA calculations and molecular dynamics studies, Front. Chem. 12 (2024) 1–14. https://doi.org/10.3389/fchem.2024.1339891. DOI: https://doi.org/10.3389/fchem.2024.1339891
[40] A. Edris, W. Ibraheem, A.A. Makki, E.M. Elshamly, R. Elhag, W. Osman, Dynamics Studies, Molecules 28 (2023) 1–19.
[41] W. Ibraheem, A.A. Makki, A.A. Alzain, Phthalide derivatives as dihydrofolate reductase inhibitors for malaria: molecular docking and molecular dynamics studies, J. Biomol. Struct. Dyn. 41 (2023) 5127–5137. https://doi.org/10.1080/07391102.2022.2080114. DOI: https://doi.org/10.1080/07391102.2022.2080114
[42] R.M. Mukhtar, N. Abdelmoniem, H.A. Elrufaie, A. Edris, H. Ghaboosh, M.A. Mahgoub, E.A.E. Garelnabi, W. Osman, A.E. Sherif, A. Ashour, K.F. Ghazawi, W.A. Samman, A.A. Alhaddad, R. Bafail, S.R.M. Ibrahim, G.A. Mohamed, A.A. Alzain, Unlocking the potential of approved drugs for the allosteric inhibition of tropomyosin-receptor kinase A using molecular docking and molecular dynamics studies, Front. Chem. 11 (2023) 1–12. https://doi.org/10.3389/fchem.2023.1205724. DOI: https://doi.org/10.3389/fchem.2023.1205724
[43] A.A. Alzain, F.A. Elbadwi, De Novo Design of Cathepsin B1 Inhibitors as Potential Anti-Schistosomal Agents Using Computational Studies, Adv. Appl. Bioinforma. Chem. 15 (2022) 29–41. https://doi.org/10.2147/AABC.S361626. DOI: https://doi.org/10.2147/AABC.S361626
[44] A.M. Ali, A.A. Makki, W. Ibraheem, M. Abdelrahman, W. Osman, A.E. Sherif, A. Ashour, S.R.M. Ibrahim, K.F. Ghazawi, W.A. Samman, A.A. Alzain, Design of Novel Phosphatidylinositol 3-Kinase Inhibitors for Non-Hodgkin’s Lymphoma: Molecular Docking, Molecular Dynamics, and Density Functional Theory Studies on Gold Nanoparticles, Molecules 28 (2023) 1–19. https://doi.org/10.3390/molecules28052289. DOI: https://doi.org/10.3390/molecules28052289
[45] D.K. Yadav, S. Kumar, Saloni, H. Singh, M.H. Kim, P. Sharma, S. Misra, F. Khan, Molecular docking, QSAR and ADMET studies of withanolide analogs against breast cancer, Drug Des. Devel. Ther. 11 (2017) 1859–1870. https://doi.org/10.2147/DDDT.S130601. DOI: https://doi.org/10.2147/DDDT.S130601
[46] T.I. Adelusi, A.Q.K. Oyedele, I.D. Boyenle, A.T. Ogunlana, R.O. Adeyemi, C.D. Ukachi, M.O. Idris, O.T. Olaoba, I.O. Adedotun, O.E. Kolawole, Y. Xiaoxing, M. Abdul-Hammed, Molecular modeling in drug discovery, Informatics Med. Unlocked 29 (2022) 100880. https://doi.org/10.1016/j.imu.2022.100880. DOI: https://doi.org/10.1016/j.imu.2022.100880
[47] A. Alamri, A. Rauf, A.A. Khalil, A. Alghamdi, A. Alafnan, A. Alshammari, F. Alshammari, J.A. Malik, S. Anwar, In Silico Screening of Marine Compounds as an Emerging and Promising Approach against Estrogen Receptor Alpha-Positive Breast Cancer, Biomed Res. Int. 2021 (2021) 1–7. https://doi.org/10.1155/2021/9734279. DOI: https://doi.org/10.1155/2021/9734279
[48] D. Craig Allred, P. Brown, D. Medina, The origins of estrogen receptor alpha-positive and estrogen receptor alpha-negative human breast cancer, Breast Cancer Res. 6 (2004) 240–245. https://doi.org/10.1186/bcr938. DOI: https://doi.org/10.1186/bcr938
[49] X. Puyang, C. Furman, G.Z. Zheng, Z.J. Wu, D. Banka, K. Aithal, S. Agoulnik, D.M. Bolduc, S. Buonamici, B. Caleb, S. Das, S.E. Fekkes, M.H. Hao, A. Hart, R. Houtman, S. Irwin, J.J. Joshi, C.K. Kim, N. Kumar, P. Kumar, G. Kuznetsov, W.G. Lai, N. Larsen, C. Mackenzie, L.A. Martin, D. Melchers, A. Moriarty, T.V. Nguyen, J. Norris, M. O’shea, S. Pancholi, S. Prajapati, S. Rajagopalan, D.J. Reynolds, V. Rimkunas, N. Rioux, R. Ribas, A. Siu, S. Sivakumar, V. Subramanian, M. Thomas, F.H. Vaillancourt, J. Wang, S. Wardell, M.J. Wick, S. Yao, L. Yu, M. Warmuth, P.G. Smith, P. Zhu, M. Korpal, Discovery of selective estrogen receptor covalent antagonists for the treatment of ERαWT and ERαMUT breast cancer, Cancer Discov. 8 (2018) 1176–1193. https://doi.org/10.1158/2159-8290.CD-17-1229. DOI: https://doi.org/10.1158/2159-8290.CD-17-1229
[50] M. Fantacuzzi, M. Agamennone, Computational Methods in the Design of Anticancer Drugs, Pharmaceuticals 17 (2024) 1–4. https://doi.org/10.3390/ph17040404. DOI: https://doi.org/10.3390/ph17040404
[51] R. Pokhrel, T. Tang, J.M. Holub, Monitoring ligand-mediated helix 12 transitions within the human estrogen receptor α using bipartite tetracysteine display, Org. Biomol. Chem. 18 (2020) 6063–6071. https://doi.org/10.1039/d0ob01234c. DOI: https://doi.org/10.1039/D0OB01234C
[52] R. Alghamdi, N. Abutaha, F.A. Almekhlafi, M.A. Wadaan, Investigation of Lycium shawii Roem . Extract, I (2021) 1–9. https://doi.org/10.18805/IJAR.BF-1751.Submitted.
[53] R. Sehrawat, P. Rathee, P. Rathee, S. Khatkar, E.K. Akkol, A. Khatkar, E. Sobarzo-Sánchez, In silico design of novel bioactive molecules to treat breast cancer with chlorogenic acid derivatives: a computational and SAR approach, Front. Pharmacol. 14 (2023) 1–24. https://doi.org/10.3389/fphar.2023.1266833. DOI: https://doi.org/10.3389/fphar.2023.1266833
[54] E.B. Mass, C.A. de Lima, M.G.M. D’Oca, J.M. Sciani, G.B. Longato, D. Russowsky, Synthesis, Selective Cytotoxic Activity against Human Breast Cancer MCF7 Cell Line and Molecular Docking of Some Chalcone-Dihydropyrimidone Hybrids, Drugs Drug Candidates 1 (2022) 3–21. https://doi.org/10.3390/ddc1010002. DOI: https://doi.org/10.3390/ddc1010002
[55] A.A. Alzain, A. Ismail, M. Fadlelmola, M.A. Mohamed, M. Mahjoub, A.A. Makki, T. Elsaman, De novo design of novel spike glycoprotein inhibitors using e-pharmacophore modeling, molecular hybridization, ADMET, quantum mechanics and molecular dynamics studies for COVID-19, Pak. J. Pharm. Sci. 35 (2022) 313–321. https://doi.org/10.36721/PJPS.2022.35.1.SUP.313-321.1.
[56] F.A.D.M. Opo, M.M. Rahman, F. Ahammad, I. Ahmed, M.A. Bhuiyan, A.M. Asiri, Structure based pharmacophore modeling, virtual screening, molecular docking and ADMET approaches for identification of natural anti-cancer agents targeting XIAP protein, Sci. Rep. 11 (2021) 1–18. https://doi.org/10.1038/s41598-021-83626-x. DOI: https://doi.org/10.1038/s41598-021-83626-x
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