Exploration of Mesyl Chalcones as Potent Inhibitor of the Proto Oncogene Erbb-2 Proliferation by Using Computational In-silico Approach
Abstract
The discovery of novel drugs was recognized as a convoluted, costly, time-consuming, & demanding process. It was found that more than 10 years and approximately 4 billion INR are required for the finding of a novel medicine through old-fashioned drug development procedures. In the pharmaceutical industry, figuring out how to lower research costs and accelerate the development process of new drugs has become a difficult and pressing question. Computer-aided drug design has become a potent and capable technique for a quicker, less expensive, and more successful approach. Molecular docking is a useful technique for estimating the structure of ligand-protein complexes. Over the past few years, computational tools for drug discovery, including antitumor therapies, have displayed a significant and exceptional power on the design of antitumor drugs. It has been found that chalcones serve as starting materials for the synthesis of a large number of organic compounds, and this moiety has a variety of pharmacological properties, including anticancer activity. The present study aims to identify a new chemical entity of mesyl chalcone as anticancer agents and analyze their binding capacities, Van der Waals potentials, and drug likeness through the molecular docking process. Physicochemical properties were calculated using Molinspiration and Swiss ADMET. The docking study was done on the crystal structure of receptor tyrosine protein kinase ErbB2. The study shown that all the compounds exposed outstanding binding energies in the active sites of the protein and can be considered potent inhibitors of the proto-oncogene ErbB-2 proliferation.
Keywords:
ADME Properties, Anticancer, HER2, Ligands, Mesyl Chalcones, Protein, Molecular Docking
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References
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Ghose, A. K., & Crippen, G. M. (1987). Atomic physicochemical parameters for three-dimensional-structure-directed quantitative structure-activity relationships. 2. Modeling dispersive and hydrophobic interactions. Journal of Chemical Information and Computer Sciences, 27(1), 21-35. https://doi.org/10.1021/ci00053a005
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Kumar, N. A., Sharmila, R., Akila, K., & Jaikumar, B. (2016). In-silico approach for the assessment of oral cancer property on Limonia acidissima. International Journal of Pharmaceutical Sciences and Research, 7(3), 1271-1275. https://doi.org/10.13040/IJPSR.0975-8232.7(3).1271-75
Lakshminarayanan, B., Kannappan, N., & Subburaju, T. (2020). Evaluation of antidiabetic and antioxidant potential of some novel ethoxylated head of α, β-unsaturated Ketones. Research Journal of Pharmacy and Technology, 13(3), 1397-1402. https://doi.org/10.5958/0974-360X.2020.00256.5
Leite, F. F., de Sousa, N. F., de Oliveira, B. H. M., Duarte, G. D., Ferreira, M. D. L., Scotti, M. T., ... & Scotti, L. (2023). Anticancer activity of chalcones and its derivatives: review and in silico studies. Molecules, 28(10), 4009. https://doi.org/10.3390/molecules28104009.
Lipinski, C. A., Lombardo, F., Dominy, B. W., & Feeney, P. J. (2012). Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Advanced Drug Delivery Reviews, 64, 4-17. https://doi.org/10.1016/j.addr.2012.09.019
Martin, Y. C. (2005). A bioavailability score. Journal of Medicinal Chemistry, 48(9), 3164-3170. https://doi.org/10.1021/jm0492002
McNutt, A. T., Li, Y., Meli, R., Aggarwal, R., & Koes, D. R. (2025). GNINA 1.3: the next increment in molecular docking with deep learning. Journal of Cheminformatics, 17(1), 28. https://doi.org/10.1186/s13321-025-00973-x
Mishra, R. K., Mishra, A., & Gupta, A. (2022). Promiscuous or Dirty drug with Multifunctional Druggability nature of Curcumin (Curcuma longa Linn.); Repurposing in propranolol withdrawal-induced OCD related Anxiety: A promising drug discovery besides One-Drug-One-Receptor approach via in silico in vivo studies. Research Journal of Pharmacy and Technology, 15(7), 2898-2908. https://doi.org/10.52711/0974-360X.2022.00484
Mishra, R., Mishra, P. S., Mazumder, R., Mazumder, A., & Chaudhary, A. (2021). Computational docking technique for drug discovery: A review. Research Journal of Pharmacy and Technology, 14(10), 5558-5562. https://doi.org/10.52711/0974-360X.2021.00968
Muegge, I., Heald, S. L., & Brittelli, D. (2001). Simple selection criteria for drug-like chemical matter. Journal of Medicinal Chemistry, 44(12), 1841-1846. https://doi.org/10.1021/jm015507e
Mukadam, M. M., & Jagdale, D. M. (2024). In silico ADME/T Prediction of Steroidal Chalcone derivatives using Swiss ADME and OSIRIS explorer. Research Journal of Pharmacy and Technology, 17(2), 843-848. https://doi.org/10.52711/0974-360X.2024.00130
Nivatya, H. K., Singh, A., Kumar, N., Sonam, Sharma, L., Singh, V., ... & Mishra, A. K. (2025). Assessing molecular docking tools: understanding drug discovery and design. Future Journal of Pharmaceutical Sciences, 11(1), 111. https://doi.org/10.1186/s43094-025-00862-y
Parameswari, P., & Devika, R. (2019). In silico molecular docking studies of quercetin compound against anti-inflammatory and anticancer proteins. Research Journal of Pharmacy and Technology, 12(11), 5305-5309. https://doi.org/10.5958/0974-360X.2019.00919.3.
Redhwan, M. A. M., Deka, G., & Varghese, M. M. (2020). Synthesis and Molecular docking studies of some new Pyrazoline derivatives for antimicrobial properties. Research Journal of Pharmacy and Technology, 13(10), 4629-4634. https://doi.org/10.5958/0974-360X.2020.00815.X
Schneider, G., & Fechner, U. (2005). Computer-based de novo design of drug-like molecules. Nature Reviews Drug Discovery, 4(8), 649-663. https://doi.org/10.1038/nrd1799
Taylor, R. D., MacCoss, M., & Lawson, A. D. (2014). Rings in drugs: Miniperspective. Journal of Medicinal Chemistry, 57(14), 5845-5859. https://doi.org/10.1021/jm4017625
Teague, S. J., Davis, A. M., Leeson, P. D., & Oprea, T. (1999). The design of leadlike combinatorial libraries. Angewandte Chemie International Edition, 38(24), 3743-3748. https://doi.org/10.1002/(SICI)1521-3773(19991216)38:24%3C3743::AID-ANIE3743%3E3.0.CO;2-U
Thomas, V. H., Bhattachar, S., Hitchingham, L., Zocharski, P., Naath, M., Surendran, N., ... & El-Kattan, A. (2006). The road map to oral bioavailability: an industrial perspective. Expert Opinion on Drug Metabolism & Toxicology, 2(4), 591-608. https://doi.org/10.1517/17425255.2.4.591.
Umar, S., Katariya, S., Soni, R., Soman, S. S., & Suresh, B. (2024). O-Allyloxy chalcone derivatives: Design, synthesis, anticancer activity, network pharmacology and molecular docking. Chemical Papers, 78(16), 8903-8917. https://doi.org/10.1007/s11696-024-03723-9
Varghese, A., Liu, J., Patterson, T. A., & Hong, H. (2025). Integrating Molecular Dynamics, Molecular Docking, and Machine Learning for Predicting SARS-CoV-2 Papain-like Protease Binders. Molecules, 30(14), 2985. https://doi.org/10.3390/molecules30142985
Veber, D. F., Johnson, S. R., Cheng, H. Y., Smith, B. R., Ward, K. W., & Kopple, K. D. (2002). Molecular properties that influence the oral bioavailability of drug candidates. Journal of Medicinal Chemistry, 45(12), 2615-2623. https://doi.org/10.1021/jm020017n
Askarzade, A., Ahmadi, S., & Almasirad, A. (2025). SMILES-based QSAR and molecular docking studies of chalcone analogues as potential anti-colon cancer. Scientific Reports, 15(1), 6573. https://doi.org/10.1038/s41598-025-91338-9
Badertscher, M., Bischofberger, K., Munk, M. E., & Pretsch, E. (2001). A novel formalism to characterize the degree of unsaturation of organic molecules. Journal of chemical information and computer sciences, 41(4), 889-893. https://doi.org/10.1021/ci000135o
Baell, J. B., & Holloway, G. A. (2010). New substructure filters for removal of pan assay interference compounds (PAINS) from screening libraries and for their exclusion in bioassays. Journal of Medicinal Chemistry, 53(7), 2719-2740. https://doi.org/10.1021/jm901137j
Baell, J., & Walters, M. A. (2014). Chemistry: Chemical con artists foil drug discovery. Nature, 513(7519), 481-483. https://doi.org/10.1038/513481a
Balakrishnan, N., Raj, J. S., & Kandakatla, N. (2014). 2D/3D-QSAR, docking and optimization of 5-substituted-1h-indazole as inhibitors of GSK-3β. International Journal of Pharmaceutical Science, 6(10), 413-420. https://journals.innovareacademics.in/index.php/ijpps/article/view/2932
Boora, N., Sindhu, S. R., Rani, R., & Verma, A. (2025). Molecular Docking and Simulation Analysis: Uncover Potential Phytochemical Inhibitors of the SARS-CoV-2 Main Protease. Journal of Advances in Biology & Biotechnology, 28(9), 1116-1132. https://doi.org/10.9734/jabb/2025/v28i92960
Bray, F., Ferlay, J., Soerjomataram, I., Siegel, R. L., Torre, L. A., & Jemal, A. (2018). Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: A Cancer Journal for Clinicians, 68(6), 394-424. https://doi.org/10.3322/caac.21492
Chen, X., Yan, C. C., Zhang, X., Zhang, X., Dai, F., Yin, J., & Zhang, Y. (2016). Drug–target interaction prediction: databases, web servers and computational models. Briefings in Bioinformatics, 17(4), 696-712. https://doi.org/10.1093/bib/bbv066
Cui, W., Aouidate, A., Wang, S., Yu, Q., Li, Y., & Yuan, S. (2020). Discovering anti-cancer drugs via computational methods. Frontiers in Pharmacology, 11, 733.https://doi.org/10.3389/fphar.2020.00733
Dahlin, J. L., & Walters, M. A. (2014). The essential roles of chemistry in high-throughput screening triage. Future Medicinal Chemistry, 6(11), 1265-1290. https://doi.org/10.4155/fmc.14.60
Egan, W. J., Merz, K. M., & Baldwin, J. J. (2000). Prediction of drug absorption using multivariate statistics. Journal of Medicinal Chemistry, 43(21), 3867-3877. https://doi.org/10.1021/jm000292e
Ghose, A. K., & Crippen, G. M. (1987). Atomic physicochemical parameters for three-dimensional-structure-directed quantitative structure-activity relationships. 2. Modeling dispersive and hydrophobic interactions. Journal of Chemical Information and Computer Sciences, 27(1), 21-35. https://doi.org/10.1021/ci00053a005
Halfar, R., Damborský, J., Marques, S. M., & Martinovič, J. (2025). Moldina: A fast and accurate search algorithm for simultaneous docking of multiple ligands. Journal of Cheminformatics, 17(1), 1-10. https://doi.org/10.1186/s13321-025-01005-4
Kodical, D. D., James, J. P., Deepthi, K., Kumar, P., Cyriac, C., & Gopika, K. V. (2020). ADMET, Molecular docking studies and binding energy calculations of Pyrimidine-2-Thiol Derivatives as Cox Inhibitors. Research Journal of Pharmacy and Technology, 13(9), 4200-4206. https://doi.org/10.5958/0974-360X.2020.00742.8
Kumar, N. A., Sharmila, R., Akila, K., & Jaikumar, B. (2016). In-silico approach for the assessment of oral cancer property on Limonia acidissima. International Journal of Pharmaceutical Sciences and Research, 7(3), 1271-1275. https://doi.org/10.13040/IJPSR.0975-8232.7(3).1271-75
Lakshminarayanan, B., Kannappan, N., & Subburaju, T. (2020). Evaluation of antidiabetic and antioxidant potential of some novel ethoxylated head of α, β-unsaturated Ketones. Research Journal of Pharmacy and Technology, 13(3), 1397-1402. https://doi.org/10.5958/0974-360X.2020.00256.5
Leite, F. F., de Sousa, N. F., de Oliveira, B. H. M., Duarte, G. D., Ferreira, M. D. L., Scotti, M. T., ... & Scotti, L. (2023). Anticancer activity of chalcones and its derivatives: review and in silico studies. Molecules, 28(10), 4009. https://doi.org/10.3390/molecules28104009.
Lipinski, C. A., Lombardo, F., Dominy, B. W., & Feeney, P. J. (2012). Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Advanced Drug Delivery Reviews, 64, 4-17. https://doi.org/10.1016/j.addr.2012.09.019
Martin, Y. C. (2005). A bioavailability score. Journal of Medicinal Chemistry, 48(9), 3164-3170. https://doi.org/10.1021/jm0492002
McNutt, A. T., Li, Y., Meli, R., Aggarwal, R., & Koes, D. R. (2025). GNINA 1.3: the next increment in molecular docking with deep learning. Journal of Cheminformatics, 17(1), 28. https://doi.org/10.1186/s13321-025-00973-x
Mishra, R. K., Mishra, A., & Gupta, A. (2022). Promiscuous or Dirty drug with Multifunctional Druggability nature of Curcumin (Curcuma longa Linn.); Repurposing in propranolol withdrawal-induced OCD related Anxiety: A promising drug discovery besides One-Drug-One-Receptor approach via in silico in vivo studies. Research Journal of Pharmacy and Technology, 15(7), 2898-2908. https://doi.org/10.52711/0974-360X.2022.00484
Mishra, R., Mishra, P. S., Mazumder, R., Mazumder, A., & Chaudhary, A. (2021). Computational docking technique for drug discovery: A review. Research Journal of Pharmacy and Technology, 14(10), 5558-5562. https://doi.org/10.52711/0974-360X.2021.00968
Muegge, I., Heald, S. L., & Brittelli, D. (2001). Simple selection criteria for drug-like chemical matter. Journal of Medicinal Chemistry, 44(12), 1841-1846. https://doi.org/10.1021/jm015507e
Mukadam, M. M., & Jagdale, D. M. (2024). In silico ADME/T Prediction of Steroidal Chalcone derivatives using Swiss ADME and OSIRIS explorer. Research Journal of Pharmacy and Technology, 17(2), 843-848. https://doi.org/10.52711/0974-360X.2024.00130
Nivatya, H. K., Singh, A., Kumar, N., Sonam, Sharma, L., Singh, V., ... & Mishra, A. K. (2025). Assessing molecular docking tools: understanding drug discovery and design. Future Journal of Pharmaceutical Sciences, 11(1), 111. https://doi.org/10.1186/s43094-025-00862-y
Parameswari, P., & Devika, R. (2019). In silico molecular docking studies of quercetin compound against anti-inflammatory and anticancer proteins. Research Journal of Pharmacy and Technology, 12(11), 5305-5309. https://doi.org/10.5958/0974-360X.2019.00919.3.
Redhwan, M. A. M., Deka, G., & Varghese, M. M. (2020). Synthesis and Molecular docking studies of some new Pyrazoline derivatives for antimicrobial properties. Research Journal of Pharmacy and Technology, 13(10), 4629-4634. https://doi.org/10.5958/0974-360X.2020.00815.X
Schneider, G., & Fechner, U. (2005). Computer-based de novo design of drug-like molecules. Nature Reviews Drug Discovery, 4(8), 649-663. https://doi.org/10.1038/nrd1799
Taylor, R. D., MacCoss, M., & Lawson, A. D. (2014). Rings in drugs: Miniperspective. Journal of Medicinal Chemistry, 57(14), 5845-5859. https://doi.org/10.1021/jm4017625
Teague, S. J., Davis, A. M., Leeson, P. D., & Oprea, T. (1999). The design of leadlike combinatorial libraries. Angewandte Chemie International Edition, 38(24), 3743-3748. https://doi.org/10.1002/(SICI)1521-3773(19991216)38:24%3C3743::AID-ANIE3743%3E3.0.CO;2-U
Thomas, V. H., Bhattachar, S., Hitchingham, L., Zocharski, P., Naath, M., Surendran, N., ... & El-Kattan, A. (2006). The road map to oral bioavailability: an industrial perspective. Expert Opinion on Drug Metabolism & Toxicology, 2(4), 591-608. https://doi.org/10.1517/17425255.2.4.591.
Umar, S., Katariya, S., Soni, R., Soman, S. S., & Suresh, B. (2024). O-Allyloxy chalcone derivatives: Design, synthesis, anticancer activity, network pharmacology and molecular docking. Chemical Papers, 78(16), 8903-8917. https://doi.org/10.1007/s11696-024-03723-9
Varghese, A., Liu, J., Patterson, T. A., & Hong, H. (2025). Integrating Molecular Dynamics, Molecular Docking, and Machine Learning for Predicting SARS-CoV-2 Papain-like Protease Binders. Molecules, 30(14), 2985. https://doi.org/10.3390/molecules30142985
Veber, D. F., Johnson, S. R., Cheng, H. Y., Smith, B. R., Ward, K. W., & Kopple, K. D. (2002). Molecular properties that influence the oral bioavailability of drug candidates. Journal of Medicinal Chemistry, 45(12), 2615-2623. https://doi.org/10.1021/jm020017n
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Lakshminarayanan, B., Parthiban, P., Vijayakumar, B., Geethapriya, C., & Kannappan, N. (2026). Exploration of Mesyl Chalcones as Potent Inhibitor of the Proto Oncogene Erbb-2 Proliferation by Using Computational In-silico Approach. International Journal of Advancement in Life Sciences Research, 9(1), 143-156. https://doi.org/https://doi.org/10.31632/ijalsr.2026.v09i01.011
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