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2000
Volume 32, Issue 1
  • ISSN: 1381-6128
  • E-ISSN: 1873-4286

Abstract

Background

Triple-negative breast cancer (TNBC) is an aggressive subtype characterized by the absence of estrogen and progesterone receptors (ER, PR) and low or absent HER2 expression, limiting treatment options. Quercetin, a flavonoid with anti-cancer properties, has the potential to be a therapeutic intervention.

Objectives

The study aimed to explore the potential of Quercetin derivatives as therapeutic agents for TNBC using several computational methods.

Methods

The study utilized PASS prediction, molecular docking, ADMET prediction, QSAR models, MD simulations, binding free energy, and DFT calculations to evaluate the efficacy of quercetin derivatives.

Results

ADMET analysis confirmed the solubility, non-carcinogenicity, and low toxicity of four quercetin derivatives: LM01, LM02, LM05, and LM10. These derivatives exhibited strong binding affinity against TNBC protein PPAR1, with binding energies of -10.6, -10.7, -11.4, and -10 kcal/mol, respectively. MD simulations confirmed their stability, with consistent RMSD values and favorable RMSF values. Post-simulation calculations and reduced HOMO-LUMO energy gaps further supported their potential as promising candidates.

Conclusion

Our computational findings suggest that quercetin derivatives, particularly LM01, LM02, and LM10, exhibit strong stability and binding affinity, positioning them as promising candidates for TNBC treatment. Further experimental validation is required to confirm their therapeutic potential.

© 2026 Bentham Science Publishers
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References

  1. Unger-SaldañaK. Challenges to the early diagnosis and treatment of breast cancer in developing countries.World J. Clin. Oncol.20145346547710.5306/wjco.v5.i3.465 25114860
     [Google Scholar]
  2. TaoZ. ShiA. LuC. SongT. ZhangZ. ZhaoJ. Breast cancer: Epidemiology and etiology.Cell Biochem. Biophys.201572233333810.1007/s12013‑014‑0459‑6 25543329
     [Google Scholar]
  3. PodoF. BuydensL.M. DeganiH. Triple-negative breast cancer: Present challenges and new perspectives.Mol. Oncol.20104320922910.1016/j.molonc.2010.04.006 20537966
     [Google Scholar]
  4. LaneD.A. AguinagaL. Blomström-LundqvistC. Cardiac tachyarrhythmias and patient values and preferences for their management: The European Heart Rhythm Association (EHRA) consensus document endorsed by the Heart Rhythm Society (HRS), Asia Pacific Heart Rhythm Society (APHRS), and Sociedad Latinoamericana de Estimulación Cardíaca y Electrofisiología (SOLEACE).Europace201517121747176910.1093/europace/euv233 26108807
     [Google Scholar]
  5. AlmansourN.M. Triple-negative breast cancer: A brief review about epidemiology, risk factors, signaling pathways, treatment and role of artificial intelligence.Front. Mol. Biosci.2022983641710.3389/fmolb.2022.836417 35145999
     [Google Scholar]
  6. HowardF.M. OlopadeO.I. Epidemiology of triple-negative breast cancer: A review.Cancer J.202127181610.1097/PPO.0000000000000500 33475288
     [Google Scholar]
  7. AkbarzadehM. VahedianV. AbudulmohesenH.Z. The evaluation of melatonin and EGF interaction on breast cancer metastasis.Horm. Mol. Biol. Clin. Investig.202445311913010.1515/hmbci‑2023‑0082 39042852
     [Google Scholar]
  8. LiX. YangJ. PengL. Triple-negative breast cancer has worse overall survival and cause-specific survival than non-triple-negative breast cancer.Breast Cancer Res. Treat.2017161227928710.1007/s10549‑016‑4059‑6 27888421
     [Google Scholar]
  9. MedinaM.A. OzaG. SharmaA. Triple-negative breast cancer: A review of conventional and advanced therapeutic strategies.Int. J. Environ. Res. Public Health2020176207810.3390/ijerph17062078 32245065
     [Google Scholar]
  10. KhalafiA. MoradianF. RafieiA. Anticancer effect of bovine lactoferrin on breast cancer cell line MCF7 and the evaluation of bax and bak genes expression involved in apoptosis.Res Mol Med20208310711610.32598/rmm.8.3.726.3
     [Google Scholar]
  11. TangJ. TangY. YangJ. HuangS. Chemoradiation and adjuvant chemotherapy in advanced cervical adenocarcinoma.Gynecol. Oncol.2012125229730210.1016/j.ygyno.2012.01.033 22307061
     [Google Scholar]
  12. BiliciA. ArslanC. AltundagK. Promising therapeutic options in triple-negative breast cancer.J. Balkan Union Oncol.2012172209222 22740196
     [Google Scholar]
  13. SharmaP. Biology and management of patients with triple-negative breast cancer.Oncologist20162191050106210.1634/theoncologist.2016‑0067 27401886
     [Google Scholar]
  14. ReyesH.D. ThielK.W. CarlsonM.J. Comprehensive profiling of EGFR/HER receptors for personalized treatment of gynecologic cancers.Mol. Diagn. Ther.201418213715110.1007/s40291‑013‑0070‑3 24403167
     [Google Scholar]
  15. ColomboM. CorsiF. FoschiD. HER2 targeting as a two-sided strategy for breast cancer diagnosis and treatment: Outlook and recent implications in nanomedical approaches.Pharmacol. Res.201062215016510.1016/j.phrs.2010.01.013 20117211
     [Google Scholar]
  16. ChaleshtoriM.M. HojatiZ. JazaeriA. HER2 testing in invasive breast cancer: A comparison between immunohistochemistry and fluorescence in situ hybridization assays.Res Mol Med202083139146
     [Google Scholar]
  17. GuptaG.K. CollierA.L. LeeD. Perspectives on triple-negative breast cancer: Current treatment strategies, unmet needs, and potential targets for future therapies.Cancers2020129239210.3390/cancers12092392 32846967
     [Google Scholar]
  18. DiasM.C. PintoD.C.G.A. SilvaA.M.S. Plant flavonoids: Chemical characteristics and biological activity.Molecules20212617537710.3390/molecules26175377 34500810
     [Google Scholar]
  19. YeungC.S. DongV.M. Catalytic dehydrogenative cross-coupling: Forming carbon-carbon bonds by oxidizing two carbon-hydrogen bonds.Chem. Rev.201111131215129210.1021/cr100280d 21391561
     [Google Scholar]
  20. MlcekJ. JurikovaT. SkrovankovaS. SochorJ. Quercetin and its anti-allergic immune response.Molecules201621562310.3390/molecules21050623 27187333
     [Google Scholar]
  21. BulbiankovaD. Díaz-PuertasR. Álvarez-MartínezF.J. Herranz-LópezM. Barrajón-CatalánE. MicolV. Hallmarks and biomarkers of skin senescence: An updated review of skin senotherapeutics.Antioxidants202312244410.3390/antiox12020444 36830002
     [Google Scholar]
  22. AkbariB. Baghaei-YazdiN. BahmaieM. AbhariM.F. The role of plant‐derived natural antioxidants in reduction of oxidative stress.Biofactors202248361163310.1002/biof.1831 35229925
     [Google Scholar]
  23. ShorobiF.M. NisaF.Y. SahaS. Quercetin: A functional food-flavonoid incredibly attenuates emerging and re-emerging viral infections through immunomodulatory actions.Molecules202328393810.3390/molecules28030938 36770606
     [Google Scholar]
  24. ChengS-C. HuangW.C. PangS.J.H. WuY.H. ChengC.Y. Quercetin inhibits the production of IL-1β-induced inflammatory cytokines and chemokines in ARPE-19 cells via the MAPK and NF-κB signaling pathways.Int. J. Mol. Sci.20192012295710.3390/ijms20122957 31212975
     [Google Scholar]
  25. SafiA. HeidarianE. AhmadiR. Quercetin synergistically enhances the anticancer efficacy of docetaxel through induction of apoptosis and modulation of PI3K/AKT, MAPK/ERK, and JAK/] STAT3 signaling pathways in MDA-MB-231 breast cancer cell line.Int. J. Mol. Cell. Med.20211011122 34268250
     [Google Scholar]
  26. WuF. ZhouY. LiL. Computational approaches in preclinical studies on drug discovery and development.Front Chem.2020872610.3389/fchem.2020.00726 33062633
     [Google Scholar]
  27. AlmatroodiS.A. AlsahliM.A. AlmatroudiA. Potential therapeutic targets of quercetin, a plant flavonol, and its role in the therapy of various types of cancer through the modulation of various cell signaling pathways.Molecules2021265131510.3390/molecules26051315 33804548
     [Google Scholar]
  28. ChimentoA. LucaD.A. D’AmicoM. AmicisD.F. PezziV. The involvement of natural polyphenols in molecular mechanisms inducing apoptosis in tumor cells: A promising adjuvant in cancer therapy.Int. J. Mol. Sci.2023242168010.3390/ijms24021680 36675194
     [Google Scholar]
  29. Ghafouri-FardS. ShabestariF.A. VaeziS. Emerging impact of quercetin in the treatment of prostate cancer.Biomed. Pharmacother.202113811154810.1016/j.biopha.2021.111548 34311541
     [Google Scholar]
  30. BrockmuellerA. SamuelS.M. MazurakovaA. BüsselbergD. KubatkaP. ShakibaeiM. Curcumin, calebin A and chemosensitization: How are they linked to colorectal cancer?Life Sci.202331812150410.1016/j.lfs.2023.121504 36813082
     [Google Scholar]
  31. TianJ. LiuR. QuQ. Role of endoplasmic reticulum stress on cisplatin resistance in ovarian carcinoma.Oncol. Lett.20171331437144310.3892/ol.2017.5580 28454274
     [Google Scholar]
  32. LiZ. WanH. ShiY. OuyangP. Personal experience with four kinds of chemical structure drawing software: Review on ChemDraw, ChemWindow, ISIS/Draw, and ChemSketch.J. Chem. Inf. Comput. Sci.20044451886189010.1021/ci049794h 15446849
     [Google Scholar]
  33. MohammedH.A. AlmahmoudS.A. El-GhalyE.S.M. Comparative anticancer potentials of taxifolin and quercetin methylated derivatives against HCT-116 cell lines: Effects of O-methylation on taxifolin and quercetin as preliminary natural leads.ACS Omega2022750466294663910.1021/acsomega.2c05565 36570308
     [Google Scholar]
  34. LaguninA. StepanchikovaA. FilimonovD. PoroikovV. PASS: Prediction of activity spectra for biologically active substances.Bioinformatics200016874774810.1093/bioinformatics/16.8.747 11099264
     [Google Scholar]
  35. KawsarS.M.A. KumerA. MuniaN.S. HosenM.A. ChakmaU. AkashS. Chemical descriptors, PASS, molecular docking, molecular dynamics and ADMET predictions of glucopyranoside derivatives as inhibitors to bacteria and fungi growth.Organic Communications202215218420310.25135/acg.oc.122.2203.2397
     [Google Scholar]
  36. AvramS. MerneaM. LimbanC. BorcanF. ChifiriucC. Potential therapeutic approaches to Alzheimer’s disease by bioinformatics, cheminformatics and predicted Adme-Tox tools.Curr. Neuropharmacol.202018869671910.2174/1570159X18666191230120053 31885353
     [Google Scholar]
  37. SunD. GaoW. HuH. ZhouS. Why 90% of clinical drug development fails and how to improve it?Acta Pharm. Sin. B20221273049306210.1016/j.apsb.2022.02.002 35865092
     [Google Scholar]
  38. Al-AzzamKM SwissADME and pkCSM webservers predictors:An integrated online platform for accurate and comprehensive predictions for in silico ADME/T properties of artemisinin and its derivatives complex use of mineral resources2023325(2)142110.31643/2023/6445.13
  39. MuslikhF.A. ADMET prediction of the dominant compound from mangosteen (Garcinia mangostana L.) using pkCSM: A computational approach.Inter J Contemp Sci2023113338
     [Google Scholar]
  40. WaltersW.P. Going further than Lipinski’s rule in drug design.Expert Opin. Drug Discov.2012729910710.1517/17460441.2012.648612 22468912
     [Google Scholar]
  41. JiD. XuM. UdenigweC.C. AgyeiD. Physicochemical characterisation, molecular docking, and drug-likeness evaluation of hypotensive peptides encrypted in flaxseed proteome.Curr. Res. Food Sci.20203415010.1016/j.crfs.2020.03.001 32914119
     [Google Scholar]
  42. PatelH.M. NoolviM.N. SharmaP. Quantitative structure-activity relationship (QSAR) studies as strategic approach in drug discovery.Med. Chem. Res.201423124991500710.1007/s00044‑014‑1072‑3
     [Google Scholar]
  43. KumerA. ChakmaU. ChandroA. Modified D-glucofuranose computationally screening for inhibitor of breast cancer and triple breast cancer: Chemical descriptor, molecular docking, molecular dynamics and QSAR.J. Chil. Chem. Soc.20226735623563510.4067/S0717‑97072022000305623
     [Google Scholar]
  44. OliveiraD.D.B. GaudioA.C. BuildQSAR: A new computer program for QSAR analysis. Quantitative structure-activity relationships.An Inter J Dev Fund Pract Asp Electroanal2000196599601
     [Google Scholar]
  45. AkashS. AoviF.I. AzadM.A.K. A drug design strategy based on molecular docking and molecular dynamics simulations applied to development of inhibitor against triple-negative breast cancer by Scutellarein derivatives.PLoS One20231810e028327110.1371/journal.pone.0283271 37824496
     [Google Scholar]
  46. LiW.H. WangF. SongG.Y. YuQ.H. DuR.P. XuP. PARP-1: A critical regulator in radioprotection and radiotherapy-mechanisms, challenges, and therapeutic opportunities.Front. Pharmacol.202314119894810.3389/fphar.2023.1198948 37351512
     [Google Scholar]
  47. WeilM.K. ChenA.P. PARP inhibitor treatment in ovarian and breast cancer.Curr. Probl. Cancer201135175010.1016/j.currproblcancer.2010.12.002 21300207
     [Google Scholar]
  48. AkashS. AoviF.I. AzadM.A.K. Computational investigation of Scutellarein derivatives as an inhibitor against triple-negative breast cancer by Quantum calculation, and drug-designed approaches.bioRxiv2023
     [Google Scholar]
  49. ChatterjeeA. RoyU.K. HaldarD. Case study and performance analysis of autodock vs autodock vina for stable drug design.J Enviro Sci Comp Sci Eng Tech2018721910.24214/jecet.B.7.2.15779
     [Google Scholar]
  50. KaplanW. LittlejohnT.G. Swiss-PDB viewer (deep view).Brief. Bioinform.20012219519710.1093/bib/2.2.195 11465736
     [Google Scholar]
  51. DallakyanS. OlsonA.J. Small-molecule library screening by docking with PyRx.Methods Mol. Biol.2015126324325010.1007/978‑1‑4939‑2269‑7_19
     [Google Scholar]
  52. FerreiraL. SantosD.R. OlivaG. AndricopuloA. Molecular docking and structure-based drug design strategies.Molecules2015207133841342110.3390/molecules200713384 26205061
     [Google Scholar]
  53. HildebrandP.W. RoseA.S. TiemannJ.K.S. Bringing molecular dynamics simulation data into view.Trends Biochem. Sci.2019441190291310.1016/j.tibs.2019.06.004 31301982
     [Google Scholar]
  54. RasheedM.A. IqbalM.N. SaddickS. Identification of lead compounds against Scm (fms10) in Enterococcus faecium using computer aided drug designing.Life (Basel)20211127710.3390/life11020077 33494233
     [Google Scholar]
  55. ShivakumarD. WilliamsJ. WuY. DammW. ShelleyJ. ShermanW. Prediction of absolute solvation free energies using molecular dynamics free energy perturbation and the OPLS force field.J. Chem. Theory Comput.2010651509151910.1021/ct900587b 26615687
     [Google Scholar]
  56. KhouryE.L. Santos-MartinsD. SasmalS. Comparison of affinity ranking using AutoDock-GPU and MM-GBSA scores for BACE-1 inhibitors in the D3R Grand Challenge 4.J. Comput. Aided Mol. Des.201933121011102010.1007/s10822‑019‑00240‑w 31691919
     [Google Scholar]
  57. McGannM.R. AlmondH.R. NichollsA. GrantJ.A. BrownF.K. Gaussian docking functions.Biopolymers2003681769010.1002/bip.10207 12579581
     [Google Scholar]
  58. GadreS.R. SureshC.H. MohanN. Electrostatic potential topology for probing molecular structure, bonding and reactivity.Molecules20212611328910.3390/molecules26113289 34072507
     [Google Scholar]
  59. CaricatoM. ScalmaniG. TrucksG.W. FrischM.J. Coupled cluster calculations in solution with the polarizable continuum model of solvation.J. Phys. Chem. Lett.20101152369237310.1021/jz1007593
     [Google Scholar]
  60. Reyes-FariasM. Carrasco-PozoC. The anti-cancer effect of quercetin: Molecular implications in cancer metabolism.Int. J. Mol. Sci.20192013317710.3390/ijms20133177 31261749
     [Google Scholar]
  61. ShabirI. PandeyK.V. ShamsR. Promising bioactive properties of quercetin for potential food applications and health benefits: A review.Front. Nutr.2022999975210.3389/fnut.2022.999752 36532555
     [Google Scholar]
  62. VázquezJ. LópezM. GibertE. HerreroE. LuqueF.J. Merging ligand-based and structure-based methods in drug discovery: An overview of combined virtual screening approaches.Molecules20202520472310.3390/molecules25204723 33076254
     [Google Scholar]
  63. GreerJ. EricksonJ.W. BaldwinJ.J. VarneyM.D. Application of the three-dimensional structures of protein target molecules in structure-based drug design.J. Med. Chem.19943781035105410.1021/jm00034a001 8164249
     [Google Scholar]
  64. FilimonovD.A. LaguninA.A. GloriozovaT.A. Prediction of the biological activity spectra of organic compounds using the PASS online web resource.Chem. Heterocycl. Compd.201450344445710.1007/s10593‑014‑1496‑1
     [Google Scholar]
  65. SchneiderP. WaltersW.P. PlowrightA.T. Rethinking drug design in the artificial intelligence era.Nat. Rev. Drug Discov.202019535336410.1038/s41573‑019‑0050‑3 31801986
     [Google Scholar]
  66. KhanT. LawrenceA.J. AzadI. RazaS. JoshiS. KhanA.R. Computational drug designing and prediction of important parameters using in silico methods-A review.Curr. Computeraided Drug Des.201915538439710.2174/1573399815666190326120006 30914032
     [Google Scholar]
  67. HoogevestV.P. LiuX. FahrA. Drug delivery strategies for poorly water-soluble drugs: The industrial perspective.Expert Opin. Drug Deliv.20118111481150010.1517/17425247.2011.614228 21895540
     [Google Scholar]
  68. SinghA. WorkuZ.A. MooterD.V.G. Oral formulation strategies to improve solubility of poorly water-soluble drugs.Expert Opin. Drug Deliv.20118101361137810.1517/17425247.2011.606808 21810062
     [Google Scholar]
  69. ChangP. Improving transformer-based molecular optimization using reinforcement learning.Available from: http:/ uu.diva-portal.org/smash/get/diva2:1601671/FULLTEXT01.pdf 2021
     [Google Scholar]
  70. GraefeE.U. WittigJ. MuellerS. Pharmacokinetics and bioavailability of quercetin glycosides in humans.J. Clin. Pharmacol. (Basel)200141549249910.1177/00912700122010366 11361045
     [Google Scholar]
  71. BjörkmanS. Prediction of the volume of distribution of a drug: Which tissue-plasma partition coefficients are needed?J. Pharm. Pharmacol.20025491237124510.1211/002235702320402080 12356278
     [Google Scholar]
  72. UpadhyayR.K. Drug delivery systems, CNS protection, and the blood brain barrier.BioMed Res. Int.2014201486926910.1155/2014/869269
     [Google Scholar]
  73. EgertS. WolfframS. Bosy-WestphalA. Daily quercetin supplementation dose-dependently increases plasma quercetin concentrations in healthy humans.J. Nutr.200813891615162110.1093/jn/138.9.1615 18716159
     [Google Scholar]
  74. PersaudD. JaagumagiR. HaytonA. Guidelines for the protection and management of aquatic sediment quality in Ontario.1993Available from:https://atrium.lib.uoguelph.ca/items/5b774d1b-ff75-4310-8346-b9c8bf360366;
     [Google Scholar]
  75. HarwoodM. Danielewska-NikielB. BorzellecaJ.F. FlammG.W. WilliamsG.M. LinesT.C. A critical review of the data related to the safety of quercetin and lack of evidence of in vivo toxicity, including lack of genotoxic/carcinogenic properties.Food Chem. Toxicol.200745112179220510.1016/j.fct.2007.05.015 17698276
     [Google Scholar]
  76. YangG.F. HuangX. Development of quantitative structure-activity relationships and its application in rational drug design.Curr. Pharm. Des.200612354601461110.2174/138161206779010431 17168765
     [Google Scholar]
  77. GeraetsL. MoonenH.J.J. BrauersK. WoutersE.F.M. BastA. HagemanG.J. Dietary flavones and flavonoles are inhibitors of poly(ADP-ribose)polymerase-1 in pulmonary epithelial cells.J. Nutr.2007137102190219510.1093/jn/137.10.2190 17884996
     [Google Scholar]
  78. ChienS.Y. WuY.C. ChungJ.G. Quercetin-induced apoptosis acts through mitochondrial- and caspase-3-dependent pathways in human breast cancer MDA-MB-231 cells.Hum. Exp. Toxicol.200928849350310.1177/0960327109107002 19755441
     [Google Scholar]
  79. GohlkeH. KlebeG. Approaches to the description and prediction of the binding affinity of small-molecule ligands to macromolecular receptors.Angew. Chem. Int. Ed.200241152644267610.1002/1521‑3773(20020802)41:152644:AID‑ANIE26443.0.CO;2‑O 12203463
     [Google Scholar]
  80. NaqviA.A.T. MohammadT. HasanG.M. HassanM.I. Advancements in docking and molecular dynamics simulations towards ligand-receptor interactions and structure-function relationships.Curr. Top. Med. Chem.201818201755176810.2174/1568026618666181025114157 30360721
     [Google Scholar]
  81. SaikiaS. BordoloiM. Molecular docking: Challenges, advances and its use in drug discovery perspective.Curr. Drug Targets201920550152110.2174/1389450119666181022153016 30360733
     [Google Scholar]
  82. KalimuthuA.K. PanneerselvamT. PavadaiP. Pharmacoinformatics-based investigation of bioactive compounds of Rasam (South Indian recipe) against human cancer.Sci. Rep.20211112148810.1038/s41598‑021‑01008‑9 34728718
     [Google Scholar]
  83. SarfrazM. BakhtM.A. AlshammariM.S. Beyond traditional medications: Exploring novel and potential inhibitors of trypanothione reductase (LmTr) of Leishmania parasites.J. Biomol. Struct. Dyn.202411410.1080/07391102.2023.2300062 38213287
     [Google Scholar]
  84. ThorsellA.G. EkbladT. KarlbergT. Structural basis for potency and promiscuity in poly (ADP-ribose) polymerase (PARP) and tankyrase inhibitors.J. Med. Chem.20176041262127110.1021/acs.jmedchem.6b00990 28001384
     [Google Scholar]
  85. SongM. LiJ.L. LiX.P. KanS.F. Targeting human Poly(ADP‐Ribose) polymerase-1 with natural medicines and its potential applications in ovarian cancer therapeutics.Arch Pharm20153481181782310.1002/ardp.201500183 26344206
     [Google Scholar]
  86. LiJ. ZhouN. CaiP. BaoJ. In silico screening identifies a novel potential PARP1 inhibitor targeting synthetic lethality in cancer treatment.Int. J. Mol. Sci.201617225810.3390/ijms17020258 26907257
     [Google Scholar]
  87. LaymanR.M. ArunB. PARP inhibitors in triple-negative breast cancer including those with BRCA mutations.Cancer J.2021271677510.1097/PPO.0000000000000499 33475295
     [Google Scholar]
  88. KarplusM. McCammonJ.A. Molecular dynamics simulations of biomolecules.Nat. Struct. Biol.20029964665210.1038/nsb0902‑646 12198485
     [Google Scholar]
  89. BignonE. DršataT. MorellC. LankašF. DumontE. Interstrand cross-linking implies contrasting structural consequences for DNA: Insights from molecular dynamics.Nucleic Acids Res.201745421882195 27986856
     [Google Scholar]
  90. BeheraS.K. MahapatraN. TripathyC.S. PatiS. Drug repurposing for identification of potential inhibitors against SARS-CoV-2 spike receptor-binding domain.Indian J. Med. Res.20211531-213214310.4103/ijmr.IJMR_1132_20 33818470
     [Google Scholar]
  91. TorresM.P.H. SousaG.L.S.C. PascuttiP.G. Structural analysis of the N-terminal fragment of the antiangiogenic protein endostatin: A molecular dynamics study.Proteins20117992684269210.1002/prot.23096 21769939
     [Google Scholar]
  92. ElebeedyD. ElkhatibW.F. KandeilA. Anti-SARS-CoV-2 activities of tanshinone IIA, carnosic acid, rosmarinic acid, salvianolic acid, baicalein, and glycyrrhetinic acid between computational and in vitro insights.RSC Advances20211147292672928610.1039/D1RA05268C 35492070
     [Google Scholar]
  93. MahmudS. UddinM.A.R. PaulG.K. Virtual screening and molecular dynamics simulation study of plant-derived compounds to identify potential inhibitors of main protease from SARS-CoV-2.Brief. Bioinform.20212221402141410.1093/bib/bbaa428 33517367
     [Google Scholar]
  94. McGibbonR.T. BeauchampK.A. HarriganM.P. MDTraj: A modern open library for the analysis of molecular dynamics trajectories.Biophys. J.201510981528153210.1016/j.bpj.2015.08.015 26488642
     [Google Scholar]
  95. HouT. WangJ. LiY. WangW. Assessing the performance of the MM/PBSA and MM/GBSA methods. 1. The accuracy of binding free energy calculations based on molecular dynamics simulations.J. Chem. Inf. Model.2011511698210.1021/ci100275a 21117705
     [Google Scholar]
  96. KumerA. SarkerM.N. PaulS. The theoretical investigation of HOMO, LUMO, thermophysical properties and QSAR study of some aromatic carboxylic acids using HyperChem programming.Inter J Chem Tech201931263710.32571/ijct.478179
     [Google Scholar]
  97. ChattarajP.K. MaitiB. SarkarU. Philicity: A unified treatment of chemical reactivity and selectivity.J. Phys. Chem. A2003107254973497510.1021/jp034707u
     [Google Scholar]
  98. NoreenS. SumrraS.H. Aminothiazole-linked metal chelates: Synthesis, density functional theory, and antimicrobial studies with antioxidant correlations.ACS Omega2021648330853309910.1021/acsomega.1c05290 34901660
     [Google Scholar]
/content/journals/cpd/10.2174/0113816128373506250414160220
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Binding Interaction and Stability Analysis of Quercetin and its Derivatives as Potential Inhibitors of Triple Negative Breast Cancer (TNBC) against PARP1 Protein: An in-silico Study
Curr. Pharm. Des. 32, 52 (2026); https://doi.org/10.2174/0113816128373506250414160220
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  • Article Type:     Research Article
Keyword(s): absorption; distribution; excretion (ADMET); HOMO-LUMO energy gap; metabolism; molecular dynamic simulation and quantitative structure-activity relationship (QSAR); quercetin/analogs & derivatives; Triple negative breast neoplasms

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