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Article . 2026
License: CC BY
Data sources: ZENODO
ZENODO
Article . 2026
License: CC BY
Data sources: Datacite
ZENODO
Article . 2026
License: CC BY
Data sources: Datacite
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IN-SILICO MOLECULAR DOCKING AND ADMET PROFILING OF SELECTED PHENOLIC COMPOUNDS AS POTENTIAL TYROSINE KINASE INHIBITOR

Authors: 1*Aman Kumar, 2Amarjit Kumar Yadav, 3Devendra Kumar Yadav, 4Jitendra Kumar, 5Rohit Mandal;

IN-SILICO MOLECULAR DOCKING AND ADMET PROFILING OF SELECTED PHENOLIC COMPOUNDS AS POTENTIAL TYROSINE KINASE INHIBITOR

Abstract

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.

Keywords

Molecular docking; Tyrosine kinase; Chlorogenic acid; In-silico drug design; ADMET; Drug-likeness.

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    influence
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Related to Research communities
Cancer Research