Published February 3, 2026 | Version v1
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IN-SILICO MOLECULAR DOCKING AND ADMET PROFILING OF SELECTED PHENOLIC COMPOUNDS AS POTENTIAL TYROSINE KINASE INHIBITOR

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Tyrosine kinases are really important in how cells signal inside, controlling stuff like growth and when cells die, and when they go wrong, it leads to cancer getting worse. I think targeting them makes sense for new cancer drugs. This study looked at some phenolic compounds to see how they bind to a tyrosine kinase enzyme and what their drug properties might be, all done on a computer. We used software like ChemDraw and ChemSketch to get the ligand structures ready. Then docked them to the protein from PDB ID 3ERT with PyRx and AutoDock. For the interactions between protein and ligand, it was BIOVIA Discovery Studio and this tool called PLIP. SwissADME helped predict if theyd work as drugs, like ADMET stuff. Out of the compounds screened, chlorogenic acid stood out with the best binding, at negative 6.1 kcal per mol. It made stable bonds with active site residues, hydrogen ones, hydrophobic, and even pi pi stacking. That seems pretty solid. The ADMET results showed okay pharmacokinetics, and it followed Lipinskis Rule of Five. So chlorogenic acid could be a good starting point, maybe test it for real as a tyrosine kinase blocker in cancer treatment. Im not totally sure about the next steps, but it feels promising. The whole in silico approach helped narrow it down without lab work yet.

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3049-3013

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References

  • S. G. Ghaleno and S. J. Hosseini, "Therapeutic potential of chlorogenic acid: A review of recent experimental and clinical studies (2022–2024)," Biomed. Pharmacother., Jan. 2024; 170: 116012.
  • Davis, B. J. Walker, and H. G. Miller, "Global resurgence of Coffea liberica: Phytochemical profile and climate resilience," Nature Food, Feb. 2024; 5(2): 112–125.
  • Tan AC, Ashley DM, López GY, Malinzak M, Friedman HS, Khasraw M. Management of glioblastoma: State of the art and future directions. CA Cancer J Clin., 2020; 70(4): 299-312. doi:10.3322/caac.21613.
  • Herbst RS, Morgensztern D, Boshoff C. The biology and management of non-small cell lung cancer. Nature, 2018; 553(7689): 446-454. doi:10.1038/nature25183.
  • Stupp R, Mason WP, Van Den Bent MJ, et al. Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. N Engl J Med., 2005; 352(10): 987-996. doi:10.1056/NEJMoa043330.
  • Siegel RL, Miller KD, Fuchs HE, Jemal A. Cancer statistics, 2022. CA Cancer J Clin., 2022; 72(1): 7-33. doi:10.3322/caac.21708.
  • Pradeep, N. et al. Biological removal of phenol from wastewaters: A mini review. Appl. Water Sci., 2015; 5: 105–112.
  • Barba FJ, Roohinejad S, Barba FJ, Roohinejad S, Jambrak AR, Granato D, Montesano D, Kovačević DB (2018) Novel food processing and extraction technologies of high-added value compounds from plant materials. Foods, 7(7): 106–106.
  • X-q Sun, Chen L, Y-z Li, W-h Li, Liu G-x, Tu Y-q, et al. Structure-based ensemble-QSAR model: a novel approach to the study of the EGFR tyrosine kinase and its inhibitors. Acta Pharmacol Sin., 2014; 35(2): 301–10.
  • Muchtaridi, M., Dermawan, D., & Yusuf, M. (2018). Molecular docking, 3D structure-based pharmacophore modeling, and ADME prediction of alpha mangostin and its derivatives against estrogen receptor alpha. Journal of Young Pharmacists, Volume 10, Nomor 3: 252–259.
  • Muti'ah R, Rahmawati EK, Dewi TJD, Firdausy AF. In Silico Prediction of The Antiangiogenesis Activity of Heliannuol Lactone sesquiterpenes Compounds from Sunflower (Heliannthus annuus L.). Indones J Cancer Chemoprevention, 2021; 12(2): 90.
  • Mutiah, R., Dewi, T. J. D., Suryadinata, A., & Qonita, K. (2022). Inhibition of Human Epidermal Growth Factor Receptor-2 (HER-2) from Pomelo (Citrus maxima) Flavonoid Compounds: An in-Silico Approach. Indonesian Journal of Cancer Chemoprevention, Volume 12, Nomor 3: 148.
  • Pangribowo, S. 2019. Beban Kanker di Indonesia. Pusat Data Dan Informasi Kementerian Kesehatan RI, 1–16.
  • Pires, D. E. V, Blundell, T. L., & Ascher, D. B. 2015. pkCSM: predicting small molecule pharmacokinetic properties using graph-based signatures (Theory- How to Enterpret pkCSM Result). PKCSM, 5.
  • Zhuang, D.; Ibrahim, A.K. Deep learning for drug discovery: A study of identifying high efficacy drug compounds using a cascade transfer learning approach. Appl. Sci., 2021; 11: 7772. [Google Scholar] [CrossRef]
  • Pu, L.; Naderi, M.; Liu, T.; Wu, H.C.; Mukhopadhyay, S.; Brylinski, M. EToxPred: A machine learning-based approach to estimate the toxicity of drug candidates. BMC Pharmacol. Toxicol, 2019; 20: 2. [Google Scholar] [CrossRef]
  • Rees, C. The Ethics of Artificial Intelligence. In IFIP Advances in Information and Communication Technology, 1st ed.; Chapman and Hall/CRC; CRC Press/Taylor & Francis Group: Boca Raton, FL, USA, 2020; 555: 55–69. ISBN 9781351251389. [Google Scholar]
  • Wess, G.; Urmann, M.; Sickenberger, B. Medicinal Chemistry: Challenges and Opportunities. Angew. Chem. Int. Ed., 2001; 40: 3341–3350. [Google Scholar] [CrossRef]
  • Chen, R.; Liu, X.; Jin, S.; Lin, J.; Liu, J. Machine learning for drug-target interaction prediction. Molecules, 2018; 23: 2208. [Google Scholar] [CrossRef] [PubMed]
  • Gómez-Bombarelli, R.; Wei, J.N.; Duvenaud, D.; Hernández-Lobato, J.M.; Sánchez-Lengeling, B.; Sheberla, D.; Aguilera-Iparraguirre, J.; Hirzel, T.D.; Adams, R.P.; Aspuru-Guzik, A. Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules. ACS Central Sci., 2018; 4: 268–276. [Google Scholar] [CrossRef]
  • Meng X-Y, Zhang H-X, Mezei M, Cui M. Molecular docking: a powerful approach for structure-based drug discovery. Curr computer-aided drug Des., 2011; 7: 146–57.
  • Jorgensen WL. The many roles of computation in drug discovery. Science, 2004; 303: 1813–8.
  • Darvas F, Keseru G, Papp A, Dorman G, Urge L, Krajcsi P (2002) In silico and ex silico ADME approaches for drug discovery. Curr Top Med Chem., 2(12): 1287–1304.
  • Uzoeto HO, Ajima JN, Arazu AV, Ibiang GO, Cosmas S, Durojaye OA (2022) Immunity evasion: consequence of the N501Y mutation of the SARS-CoV-2 spike glycoprotein. J Genet Eng Biotechnol, 20(1): 1–5.
  • Bruno A, Costantino G, Sartori L, Radi M (2019) The in silico drug discovery toolbox: applications in lead discovery and optimization. Curr Med Chem., 26(21): 3838–3873.
  • Becker OM, Marantz Y, Shacham S, Inbal B, Heifetz A, Kalid O, Bar-Haim S, Warshaviak D, Fichman M, Noiman S (2004) G protein-coupled receptors: in silico drug discovery in 3D. Proc Natl Acad Sci U S A, 101(31): 11304–11309.
  • Sacan A, Ekins S, Kortagere S (2012) Applications and limitations of in silico models in drug discovery. In: Larson RS (ed) Bioinformatics and drug discovery (Methods in molecular biology), vol 910. Springer, 87–124. https://doi.org/10.1007/978-1-61779-965-5_6
  • Fadahunsi AA, Uzoeto HO, Okoro NO, Cosmas S, Durojaye OA, Odiba AS (2024) Revolutionizing drug discovery: an AI-powered transformation of molecular docking. Med Chem Res., 33: 2187–2203. https://doi.org/10.1007/s00044-024-03253-9
  • Kharkar PS, Warrier S, Gaud RS (2014) Reverse docking: a powerful tool for drug repositioning and drug rescue. Future Med Chem., 6(3): 333–342.
  • Abraham MJ, Murtola T, Schulz R, et al. GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX, 2015; 1: 19–25.
  • Agahi F, Juan C, Font G, et al. In silico methods for metabolomic and toxicity prediction of zearalenone, α-zearalenone and β-zearalenone. Food Chem Toxicol, 2020; 146: 111818.
  • Amadei A, Linssen AB, Berendsen HJ. Essential dynamics of proteins. Proteins, 1993; 17: 412–425.
  • Amir M, Kumar V, Dohare R, et al. Investigating architecture and structure-function relationships in cold shock DNA-binding domain family using structural genomics-based approach. Int J Biolog Macromol, 2019; 133: 484–494.
  • Abraham MJ, Murtola T, Schulz R, et al. GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX, 2015; 1: 19–25.
  • G, et al. In silico methods for metabolomic and toxicity prediction of zearalenone, α-zearalenone and β-zearalenone. Food Chem Toxicol, 2020; 146: 111818.
  • Amadei A, Linssen AB, Berendsen HJ. Essential dynamics of proteins. Proteins, 1993; 17: 412–425.
  • Anjum F, Ali F, Mohammad T, et al. Discovery of natural compounds as potential inhibitors of human carbonic anhydrase II: An integrated virtual screening, docking, and molecular dynamics simulation study. OMICS, 2021a; 25: 513–524.
  • Araujo SC, Maltarollo VG, Honorio KM. Computational studies of TGF-βRI (ALK-5) inhibitors: Analysis of the binding interactions between ligand-receptor using 2D and 3D techniques. Eur J Pharm Sci., 2013; 49: 542–549.