Main Article Content


The use of antibiotic drugs requires close supervision that patients take antibiotics according to the rules. Irregular antibiotic use led to increased ADR cases (Antibiotic Drug-resistant). ADR is when an individual becomes resistant to an antibiotic drug that cannot kill bacteria. The high number of ADR cases prompted drug discovery to be implemented in analysis for Antibiotic candidates with good effectiveness through the Molecular Docking approach. The search for candidate test compounds as antibiotics were performed using the pharmacophore modelling method and molecular docking. And piperine, withaferin, has some of the same amino acids Ala101, Val103, Glu166, Trp165, and Leu102. Based on the prediction of the promising potential test ligand compound is Corosolic acid. In addition to assessing drug-likeness, pharmacokinetic and toxicity parameters, corosolic acid also has the lowest binding energy among other compounds. Through a textual bioinformatics approach, molecular docking simulations can be used as a first step in the search for new drug candidates in silico by considering various aspects, starting from the physicochemical properties of protein-ligand compounds and the environment. Analysis during the docking process to ADMETOX is an analysis to see the effectiveness and in silico compound safety.  


Antibiotic resistance, urinary tract infections, molecular docking

Article Details

How to Cite
Dwininda W, Erlina L, Paramita RI, Fadillah F, Dwira S, Fatriansyah JF. Ligand Based Pharmacophore Modelling, Virtual Screening, Molecular Docking, and ADMETOX of Natural Compounds as Antibiotic Candidates against Urinary Tract Infections (UTI). EKSAKTA [Internet]. 2023Jun.30 [cited 2024May28];24(02):193-206. Available from:


  1. B. Singh. R. Tilak. R. K. Srivastava. D. Katiyar. and R. S. Chauhan.(2014).Urinary tract infection and its risk factors in women: An appraisal. J. Pure Appl. Microbiol.. vol. 8. no. 5. pp. 4155–4162.
  2. Z. Muslim. A. Novrianti. and D. Irnameria. (2020).Resistance Test Of Bacterial Cause Of Urinary Tract Infection Against Ciprofloxacin and Ceftriaxone Antibiotics. Teknol. dan Seni Kesehat.. vol. 11. no. 2. pp. 203–212.
  3. N. Abou Heidar. J. Degheili. A. Yacoubian. and R. Khauli.(2019). Management of urinary tract infection in women: A practical approach for everyday practice. Urol. Ann.. vol. 11. no. 4. pp. 339–346.
  4. D. M. Drekonja. B. Trautner. C. Amundson. M. Kuskowski. and J. R. Johnson.(2021). Effect of 7 vs 14 Days of Antibiotic Therapy on Resolution of Symptoms among Afebrile Men with Urinary Tract Infection: A Randomized Clinical Trial. JAMA - J. Am. Med. Assoc.. vol. 326. no. 4. pp. 324–331.
  5. C. I. Kang et al.. (2018). Clinical practice guidelines for the antibiotic treatment of community-acquired urinary tract infections. Infect. Chemother.. vol. 50. no. 1. pp. 67–100.
  6. V. B. A. R. G. M. Cascella3.(2022). Third Generation Cephalosporins. StatPearl Publishing. January 13, 2023.
  7. C. Díaz-Brochero. M. C. Valderrama-Rios. L. C. Nocua-Báez. and J. A. Cortés.(2022). First-generation cephalosporins for the treatment of complicated upper urinary tract infection in adults: A systematic literature review. Int. J. Infect. Dis.. vol. 116. pp. 403–410.
  8. Y. Merga Duffa. K. Terfa Kitila. D. Mamuye Gebretsadik. and A. Bitew. (2018). Prevalence and Antimicrobial Susceptibility of Bacterial Uropathogens Isolated from Pediatric Patients at Yekatit 12 Hospital Medical College. Addis Ababa. Ethiopia. Int. J. Microbiol.. vol. 2018. no. Cxm.
  9. WHO. (2022). Antimicrobial Resistance. February17,2023.
  10. W. Wang et al.. (2018). Antibiotic resistance : a rundown of a global crisis. Infect. Drug Resist.. vol. 11. pp. 1645–1658.
  11. M. Saha and A. Sarkar.(2021). Review on Multiple Facets of Drug Resistance: A Rising Challenge in the 21st Century. J. Xenobiotics. vol. 11. no. 4. pp. 197–214.
  12. S. Brogi. T. C. Ramalho. K. Kuca. J. L. Medina-Franco. and M. Valko.(2020). Editorial: In silico Methods for Drug Design and Discovery.Front. Chem.. vol. 8. no. August. pp. 1–5.
  13. D. Schaller et al..(2020). Next generation 3D pharmacophore modeling. Wiley Interdiscip. Rev. Comput. Mol. Sci.. vol. 10. no. 4. pp. 1–20.
  14. D. Giordano. C. Biancaniello. M. A. Argenio. and A. Facchiano.(2022). Drug Design by Pharmacophore and Virtual Screening Approach.Pharmaceuticals. vol. 15. no. 5. pp. 1–16.
  15. F. A. D. M. Opo. M. M. Rahman. F. Ahammad. I. Ahmed. M. A. Bhuiyan. and A. M. Asiri.(2021). Structure based pharmacophore modeling. virtual screening. molecular docking and ADMET approaches for identification of natural anti-cancer agents targeting XIAP protein. Sci. Rep.. vol. 11. no. 1. pp. 1–18.
  16. M. H. R. Molla et al..(2023). Integrative Ligand-Based Pharmacophore Modeling. Virtual Screening. and Molecular Docking Simulation Approaches Identified Potential Lead Compounds against Pancreatic Cancer by Targeting FAK1. Pharmaceuticals. vol. 16. no. 1. p. 120.
  17. C. D. D. Methods. (2018). HHS Public Access. pp. 85–106. 2018.
  18. F. Maryam. H. Mukhtar. I. Bibi. M. Rizwan. S. Khan. and A. Mehmood.(2019). Ligand Based Pharmacophore Modelling . Virtual Screening And Molecular Ligand Based Pharmacophore Modelling . Virtual Screening And Molecular Docking Of Novel Compounds Against Diabetes. no. August.
  19. M. Réau. F. Langenfeld. J. F. Zagury. N. Lagarde. and M. Montes. (2018). Decoys selection in benchmarking datasets: Overview and perspectives.” Front. Pharmacol.. vol. 9. no. JAN.
  20. V. G. Maltarollo. J. C. Gertrudes. P. R. Oliveira. and K. M. Honorio. (2015). Applying machine learning techniques for ADME-Tox prediction: A review.” Expert Opin. Drug Metab. Toxicol.. vol. 11. no. 2. pp. 259–271..
  21. A. Mishra and S. Dey. (2019). Molecular docking studies of a cyclic octapeptide-cyclosaplin from sandalwood.” Biomolecules. vol. 9. no. 11. pp. 1–18.
  22. N. K. Prasad. V. Kanakaveti. S. Eadlapalli. R. Vadde. A. P. Meetei. and V. Vindal. (2013). Ligand-based pharmacophore modeling and virtual screening of RAD9 inhibitors. J. Chem.. vol. 2013.
  23. M. T. Muhammed and E. Aki-Yalcin. (2021). Pharmacophore modeling in drug discovery: Methodology and current status.J. Turkish Chem. Soc. Sect. A Chem.. vol. 8. no. 3. pp. 749–762.
  24. N. Moussa. A. Hassan. and S. Gharaghani.(2021). Pharmacophore model. docking. QSAR. and molecular dynamics simulation studies of substituted cyclic imides and herbal medicines as COX-2 inhibitors.Heliyon. vol. 7. no. 4. p. e06605.
  25. N. Thangavel and M. Albratty.(2022). Pharmacophore model-aided virtual screening combined with comparative molecular docking and molecular dynamics for identification of marine natural products as SARS-CoV-2 papain-like protease inhibitors. Arab. J. Chem.. vol. 15. no. 12. p. 104334.
  26. N. El-Hachem. B. Haibe-Kains. A. Khalil. F. H. Kobeissy. and G. Nemer.(2017). AutoDock and AutoDockTools for protein-ligand docking: Beta-site amyloid precursor protein cleaving enzyme 1(BACE1) as a case study.Methods Mol. Biol.. vol. 1598. pp. 391–403.
  27. J. Nurhan. A. D.. Gani. M. A.. Maulana. S.. Siswodihardjo. S.. Ardianto. C.. & Khotib. (2022). Molecular Docking Studies for Protein-Targeted Drug Development in SARS-CoV-2.
  28. A. Fadlan and Y. R. Nusantoro.(2021). The Effect of Energy Minimization on The Molecular Docking of Acetone-Based Oxindole Derivatives. JKPK (Jurnal Kim. dan Pendidik. Kim.. vol. 6. no. 1. p. 69..
  29. H. Setiawan and M. I. Irawan. (2017). Kajian Pendekatan Penempatan Ligan Pada Protein Menggunakan Algoritma Genetika.” J. Sains dan Seni ITS. vol. 6. no. 2. pp. 2–6.
  30. A. Castro-Alvarez. A. M. Costa. and J. Vilarrasa. (2017). The Performance of several docking programs at reproducing protein-macrolide-like crystal structures.Molecules. vol. 22. no. 1. 2017.
  31. J. L. Velázquez-Libera. F. Durán-Verdugo. A. Valdés-Jiménez. A. Valdés-Jiménez. G. Núñez-Vivanco. and J. Caballero. (2020). LigRMSD: A web server for automatic structure matching and RMSD calculations among identical and similar compounds in protein-ligand docking. Bioinformatics. vol. 36. no. 9. pp. 2912–2914.
  32. P. Maya. W. Mahayasih. Harizal. Herman. and I. Ahmad.(2021). In silico identification of natural products from Centella asiatica as severe acute respiratory syndromecoronavirus 2 main protease inhibitor.J. Adv. Pharm. Technol. Res.. vol. 12. no. 3. pp. 261–266.
  33. K. Chaudhary. G. Pandey. M. Godar. R. Gautam. and S. Gurung.(2015). Efficacy of Cefixime in the Treatment of. Effic. Cefixime Treat. Urin. Tract Infect.. vol. 4. no. June.
  34. N. Das. J. Madhavan. A. Selvi. and D. Das. (2019). An overview of cephalosporin antibiotics as emerging contaminants: a serious environmental concern. 3 Biotech. vol. 9. no. 6. pp. 1–14.
  35. I. Widiasti. (2022). Analysis and Visualization Of Molecular Docking 2hi4 Protein Analysis And Visualization Of Molecular Docking 2HI4 Protein. Indones. J. Med. Chem. Bioinforma.. vol. 1. no. 1. pp. 1–5. 2
  36. V. K. Sahu. R. K. Singh. and P. P. Singh. (2022). Extended Rule of Five and Prediction of Biological Activity of peptidic HIV-1-PR Inhibitors.Trends J. Sci. Res.. vol. 1. no. 1. pp. 20–42.
  37. V. Ivanović. M. Rančić. B. Arsić. and A. Pavlović. (2020). Lipinski’s rule of five. famous extensions and famous exceptions.Pop. Sci. Artic.. vol. 3. no. 1. pp. 171–177.
  38. Barbezan. (2017). Ames Test to Detect Mutagenicity of 2‐Alkylcyclobutanones A Review.pdf. Journal of Food Science .
  39. E. Walum. (1998). Acute oral toxicity.Environ. Health Perspect.. vol. 106. no. SUPPL. 2. pp. 497–503. 1998. February 20,2023.
  40. J. Kim. S. Park. Y. K. Shin. H. Kang. and K. Y. Kim. (2018). In vitro antibacterial activity of macelignan and corosolic acid against the bacterial bee pathogens Paenibacillus larvae and Melissococcus plutonius. Acta Vet. Brno. vol. 87. no. 3. pp. 277–284.

Most read articles by the same author(s)