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Abstract

Benzimidazole plays an important role in the medicinal chemistry and drug discoverx with many pharmacological activities which have made an indispensable anchor for discovery of novel therapeutic agents. Substitution of benzimidazole nucleus is an important synthetic strategy in the drug discovery process. Therapeutic properties of the benzimidazole related drugs have encouraged the medicinal chemists to synthesize novel therapeutic agents Therefore, it is required to couple the latest information with the earliest information to understand the status of benzimidazole nucleus in drug discovery. In the present review, benzimidazole derivatives with different pharmacological activities are described on the basis of substitution pattern around the nucleus with an aim to help medicinal chemists for the development of SAR on benzimidazoles for each activity. This article aims to review the work reported, chemistry and pharmacological activities of benzimidazole derivatives during past years.

Keywords

Benzimidazole derivatives, Molecular docking, ADMET, Drug-likeness, Lipinski rule, Pharmacokinetics, Toxicity profiling, 2VH1 protein, Albendazole, Drug design, antimicrobial activity, antifungal activity.

Introduction

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Benzimidazole is an important heterocyclic compound widely recognized for its diverse pharmacological   activities, including antimicrobial, antifungal, antiviral, and antiparasitic properties. Due to its structural similarity with naturally occurring nucleotides, benzimidazole derivatives have gained significant attention in medicinal chemistry for the development of novel therapeutic agents. In recent years, computational approaches such as molecular docking and ADMET prediction have become essential tools in drug discovery, allowing rapid screening of compounds and reducing experimental costs. These techniques help in understanding the binding interactions between ligands and target proteins, as well as predicting pharmacokinetic and toxicity profiles. In this study, a series of benzimidazole derivatives (CB-1 to CB-8) were synthesized using different substituted aldehydes. The compounds were evaluated for their binding affinity against the target protein (PDB ID: 2VH1) through molecular docking studies. Furthermore, drug-likeness, pharmacokinetics, and toxicity assessments were performed to determine their suitability as potential drug candidates. The objective of this work is to identify promising benzimidazole-based compounds with enhanced biological activity and favorable safety profiles, which may serve as lead molecules for future pharmaceutical development.

LITERATURE REVIEW

Patel et al. (2018): - reported benzimidazole derivatives with significant antibacterial activity against S. aureus and E. coli, enhanced by halogen substitution.

Khan et al. (2019): - demonstrated antifungal potential of nitro-substituted benzimidazoles, showing comparable activity to fluconazole.

Singh et al. (2020): - highlighted that benzimidazole analogs inhibit DNA-binding enzymes and microbial topoisomerases.

De Clercq (2021): - suggested benzimidazole derivatives as promising scaffolds for designing broad-spectrum antimicrobial agents.

These studies highlight that substitutions at C-2 and N-1 positions of benzimidazole strongly influence antimicrobial activity.

SCHEME OF DESIGN

  • Synthesis CB-B

METHOD AND MATERIAL:

Procedure for Synthesis of Benzimidazole:

1. Dissolve 27g of o-phenylenediamine in a 250mL round-bottom flask.

2. Add 17.5g of formic acid to the solution.

3.Heat the mixture at 100°C for 2 hours using a water bath.

4. Allow the mixture to cool, then slowly add a 10% sodium hydroxide solution while     constantly rotating the flask, until the mixture reaches a slightly alkaline ph.

5. Filter the resulting crude benzimidazole using a vacuum pump and wash it with ice-cold water.

6. Repeat the washing step with an additional 25mL of cold water to ensure complete  purification

Fig1. Boiling Method

RECRYSTALLIZATION

1. To purify the synthesized product, dissolve it in 400 mL of boiling water.

2. Add 2 g of activated carbon to remove impurities and heat the mixture for 15 minutes.

3. Filter the solution rapidly through a preheated Buchner funnel under vacuum.

4. Allow the filtrate to cool to approximately 10°C, then collect the crystallized benzimidazole by filtration. 5. Wash the product with 25 mL of cold water and dry it at 100°C. This recrystallization process yields 25 g of pure benzimidazole, characterized by a melting point of 171-172°C.

Fig.2 Heating Phenylenediamine

Fig.3 After Heating

Fig.4 Final Product

PROCEDURE OF TLC(THIN LAYER CHROMATOGRAPHY

  1. PREPARATION OF MOBILE PHASE :

Prepare the mobile phase by mixing Chloroform and Methanol in a suitable ratio.

Commonly used ratios:

9:1 (Chloroform: Methanol) – for non-polar to moderately polar compounds

8:2 or 7:3 – for more polar compounds

Mix well and pour into the TLC chamber to a depth of about 0.5–1 cm.

Close the chamber and allow it to saturate for 10–15 minutes.

Fig.5 Mobile Phase

  1. PREPARATION OF SAMPLE :

1). Dissolve a small quantity of the sample in a suitable solvent (often methanol or chloroform).

2). The solution should be clear and dilute.

Fig.6 Spotting of sample

  1. PROCEDURE OF SPOTTING SAMPLE :

1.Take a TLC plate.

2.Draw a pencil line about 1–1.5 cm from the bottom (baseline).

3.Using a capillary tube, apply a small spot of the sample on the baseline.

4. Allow the spot to dry completely (do not blow).

5. Development of TLC Plate

6. Carefully place the TLC plate vertically in the developing

  1. DRYING THE PLATE :

1.Remove the plate and immediately mark the solvent front with a pencil.

2. Allow the plate to dry at room temperature.

  1. DETECTION OF SPOTS:
  1. Observe the plate under: UV light (254 nm or 366 nm)

OR

2. Spray with suitable reagent (iodine vapour, anisaldehyde, ninhydrin, Dragendorff’s reagent etc.)

3. Mark the visible spots.

Fig.7 Detection of spot

  1. CALCULATION OF RF VALUE

𝑅𝑓=        Distance travelled by solute                          

             Distance travelled by solvent front

1). Distance From Baseline to Spot Centre = 4.5cm

2). Distance from Baseline to Solvent Front = 6.2cm

  R𝑓 = 4.5   = 0.72

           6.5

COMPOUND SYNTHESIZED:

  1. Compound: -

IUPAC Name-: 2-(4-chlorophenyl)-1H-benzimidazole

Molecular Formula- C13H9CIN2

Molecular weight-228.68g/mol

Structure:

  1. Compound: -

IUPAC Name-: 2-(4-nitrophenyl)-1H-benzimidazole

Molecular Formula-C13H9N302

Molecular weight-239.23g/mol

Structure-

  1. Compound-

IUPAC Name-: 2-(2-hydroxyphenyl)-1H-benzimidazole

Molecular Formula – C13H10N2O

Molecular weight- 210.23g/mol

Structure-

  1. Compound-

IUPAC Name-: 2-(4-methoxyphenyl)-1H-benzimidazole

Molecular Formula-C14H12N2O

Molecular weight- 224.26g/mol

Structure-

  1. Compound-

IUPAC Name-: 2-(2-bromophenyl)-1H-benzimidazole

Molecular Formula- C13H9BrN2

Molecular weight- 273.13g/mol

Structure-

  1. Compound-

IUPAC Name-:1-methyl-2-phenylbenzimidazole

Molecular Formula-C14H12N2

Molecular weight-208.26g/mol

Structure-

ONE CLICK DOCKING ;-

  1. Compound: -  IUPAC Name-: 2-(4-chlorophenyl)-1H-benzimidazole
  1. Compound: - IUPAC Name-: 2-(4-nitrophenyl)-1H-benzimidazole
  1. Compound-   IUPAC Name-: 2-(2-hydroxyphenyl)-1H-benzimidazole
  1. Compound-   IUPAC Name-: 2-(4-methoxyphenyl)-1H-benzimidazole
  1. Compound- IUPAC Name-: 2-(2-bromophenyl)-1H-benzimidazole
  1. Compound- IUPAC Name-:1-methyl-2-phenylbenzimidazole

Protein (PDB:2VH1)

REFERENCES

  1. Azizian H, Pedrood K, Moazzam A, et al. Docking study, molecular dynamic, synthesis, and ADMET prediction of benzimidazole derivatives. Scientific Reports. 2022;12:14870.
  2. Moulishankar A, Sundarrajan T. Pharmacophore, QSAR, molecular docking and ADMET study of benzimidazole derivatives. Chemical Physics Impact. 2024;8:100512.
  3. Karthick K, Abishek K, Jemima EA. Protein kinase inhibition and docking study of benzimidazole derivatives. Cancer Informatics. 2024.
  4. Design and computational study of benzimidazole derivatives as anti-SARS-CoV-2 agents. Journal of Molecular Structure. 2024;1306:137940.
  5. Synthesis and ADMET evaluation of benzimidazole derivatives. Materials Today: Proceedings. 2022;67:598–604.
  6. Bis-Schiff base benzimidazole derivatives: docking and ADMET study. Computational Biology and Chemistry. 2025;118:108497.
  7. Lipinski CA. Drug-like properties and the rule of five. Advanced Drug Delivery Reviews. 2001;46:3–26.
  8. Veber DF, Johnson SR, Cheng HY, et al. Molecular properties influencing bioavailability. Journal of Medicinal Chemistry. 2002;45:2615–2623.
  9. Trott O, Olson AJ. AutoDock Vina: improving docking. Journal of Computational Chemistry. 2010;31:455–461.
  10. Morris GM, Huey R, Olson AJ. AutoDock docking software. Journal of Computational Chemistry. 2009;30:2785–2791.
  11. Refaat HM. Synthesis and biological evaluation of benzimidazole derivatives. European Journal of Medicinal Chemistry. 2010;45:2949–2956
  12. Singh H, Kapoor VK. Medicinal chemistry of benzimidazoles. Progress in Medicinal Chemistry. 1989;26:1–40.
  13. El-Gohary NS, Shaaban MI. Benzimidazole derivatives as antimicrobial agents. European Journal of Medicinal Chemistry. 2013;63:185–195.
  14. Kumar A, Sharma S. Benzimidazole derivatives as anticancer agents. Bioorganic & Medicinal Chemistry. 2012;20:489– 496.
  15. Patel RV, Patel PK. Benzimidazole derivatives pharmacological profile. European Journal of Medicinal Chemistry. 2014;74:487–505.
  16. Kitchen DB, Decornez H, Furr JR, Bajorath J. Docking and scoring in drug discovery. Nature Reviews Drug Discovery. 2004;3:935–949.
  17. Meng XY, Zhang HX, Mezei M. Molecular docking review. Current Computer-Aided Drug Design. 2011;7:146–157.
  18. Lionta E, Spyrou G, Vassilatis DK. Docking in drug discovery. Current Topics in Medicinal Chemistry.    2014;14:1923– 1938.
  19. Ferreira LG, Santos RN. Molecular docking and drug discovery. Molecules. 2015;20:13384–13421.
  20. Pagadala NS, Syed K, Tuszynski J. Software for molecular docking. Biophysical Reviews. 2017;9:91–102.
  21. Di L. Drug-like properties and ADMET. Current Opinion in Chemical Biology. 2015;24:113–119.
  22. Ekins S, Mestres J, Testa B. ADMET approaches. British Journal of Pharmacology. 2007;152:21–37.
  23. Hou TJ, Wang JM. ADMET prediction methods. Journal of Chemical Information and Modeling. 2007;47:460–463.
  24. Kirchmair J, Göller AH. Predicting drug metabolism. Nature Reviews Drug Discovery. 2015;14:387–404.
  25. Pires DEV, Blundell TL. pkCSM ADMET prediction. Journal of Medicinal Chemistry. 2015;58:4066–4072.
  26. Ghose AK, Viswanadhan VN. Drug-likeness filters. Journal of Combinatorial Chemistry. 1999;1:55–68.
  27. Egan WJ, Merz KM. Prediction of absorption. Journal of Medicinal Chemistry. 2000;43:3867–3877.
  28. Congreve M, Carr R. Fragment-based drug discovery. Drug Discovery Today. 2003;8:876–877.
  29. Spasov AA, Yozhitsa IN. Benzimidazole pharmacology. Pharmaceutical Chemistry Journal. 1999;33:232–243.
  30. Bansal Y, Silakari O. Benzimidazole derivatives in medicinal chemistry. Bioorganic & Medicinal Chemistry.           2012;20:6208–6236.
  31. Gupta S, Kapoor V. Antimicrobial benzimidazoles. Medicinal Chemistry Research. 2014;23:307–321.
  32. Fischer E. Lock and key model. Berichte der Deutschen Chemischen Gesellschaft. 1894.
  33. Koshland DE. Induced fit theory. Proceedings of the National Academy of Sciences. 1958;44:98–104.
  34. Goodsell DS, Olson AJ. Binding interactions. Structure. 1990;1:195–201.
  35. Sliwoski G, Kothiwale S. Computational drug discovery. Pharmacological Reviews. 2014;66:334–395.
  36. Danel T, Podlewska S. Docking-based drug discovery. Journal of Chemical Information and Modeling. 2023.
  37. Banerjee P, Eckert AO. ProTox-II toxicity prediction. Nucleic Acids Research. 2018;46:W257–W263.
  38. Wu Z, Lei T. ADMET prediction tools. Journal of Cheminformatics. 2018;10:1–11.
  39. Cheng F, Li W. Toxicity prediction models. Journal of Chemical Information and Modeling. 2012;52:3099–3105.
  40. Patrick GL. An Introduction to Medicinal Chemistry. 5th ed.
  41. Silverman RB. The Organic Chemistry of Drug Design.
  42. Wermuth CG. The Practice of Medicinal Chemistry.
  43. Zhang S, Yan Z. ADMET prediction using machine learning. Journal of Chemical Information and Modeling. 2022.
  44. Vardhan S. Docking study for SARS-CoV-2. Bioinformatics. 2020.
  45. Abdolmaleki A. QSAR and docking approaches. Current Drug Targets. 2017.
  46.  Lionta E. Drug design strategies. Current Topics in Medicinal Chemistry. 2014.
  47. Ferreira LG. Docking and virtual screening. Molecules. 2015.
  48. Pagadala NS. Docking tools review. Biophysical Reviews. 2017.
  49. Kirchmair J. Drug metabolism prediction. Nature Reviews Drug Discovery. 2015.
  50. Di L. ADMET and drug design principles. Current Opinion in Chemical Biolo

Reference

  1. Azizian H, Pedrood K, Moazzam A, et al. Docking study, molecular dynamic, synthesis, and ADMET prediction of benzimidazole derivatives. Scientific Reports. 2022;12:14870.
  2. Moulishankar A, Sundarrajan T. Pharmacophore, QSAR, molecular docking and ADMET study of benzimidazole derivatives. Chemical Physics Impact. 2024;8:100512.
  3. Karthick K, Abishek K, Jemima EA. Protein kinase inhibition and docking study of benzimidazole derivatives. Cancer Informatics. 2024.
  4. Design and computational study of benzimidazole derivatives as anti-SARS-CoV-2 agents. Journal of Molecular Structure. 2024;1306:137940.
  5. Synthesis and ADMET evaluation of benzimidazole derivatives. Materials Today: Proceedings. 2022;67:598–604.
  6. Bis-Schiff base benzimidazole derivatives: docking and ADMET study. Computational Biology and Chemistry. 2025;118:108497.
  7. Lipinski CA. Drug-like properties and the rule of five. Advanced Drug Delivery Reviews. 2001;46:3–26.
  8. Veber DF, Johnson SR, Cheng HY, et al. Molecular properties influencing bioavailability. Journal of Medicinal Chemistry. 2002;45:2615–2623.
  9. Trott O, Olson AJ. AutoDock Vina: improving docking. Journal of Computational Chemistry. 2010;31:455–461.
  10. Morris GM, Huey R, Olson AJ. AutoDock docking software. Journal of Computational Chemistry. 2009;30:2785–2791.
  11. Refaat HM. Synthesis and biological evaluation of benzimidazole derivatives. European Journal of Medicinal Chemistry. 2010;45:2949–2956
  12. Singh H, Kapoor VK. Medicinal chemistry of benzimidazoles. Progress in Medicinal Chemistry. 1989;26:1–40.
  13. El-Gohary NS, Shaaban MI. Benzimidazole derivatives as antimicrobial agents. European Journal of Medicinal Chemistry. 2013;63:185–195.
  14. Kumar A, Sharma S. Benzimidazole derivatives as anticancer agents. Bioorganic & Medicinal Chemistry. 2012;20:489– 496.
  15. Patel RV, Patel PK. Benzimidazole derivatives pharmacological profile. European Journal of Medicinal Chemistry. 2014;74:487–505.
  16. Kitchen DB, Decornez H, Furr JR, Bajorath J. Docking and scoring in drug discovery. Nature Reviews Drug Discovery. 2004;3:935–949.
  17. Meng XY, Zhang HX, Mezei M. Molecular docking review. Current Computer-Aided Drug Design. 2011;7:146–157.
  18. Lionta E, Spyrou G, Vassilatis DK. Docking in drug discovery. Current Topics in Medicinal Chemistry.    2014;14:1923– 1938.
  19. Ferreira LG, Santos RN. Molecular docking and drug discovery. Molecules. 2015;20:13384–13421.
  20. Pagadala NS, Syed K, Tuszynski J. Software for molecular docking. Biophysical Reviews. 2017;9:91–102.
  21. Di L. Drug-like properties and ADMET. Current Opinion in Chemical Biology. 2015;24:113–119.
  22. Ekins S, Mestres J, Testa B. ADMET approaches. British Journal of Pharmacology. 2007;152:21–37.
  23. Hou TJ, Wang JM. ADMET prediction methods. Journal of Chemical Information and Modeling. 2007;47:460–463.
  24. Kirchmair J, Göller AH. Predicting drug metabolism. Nature Reviews Drug Discovery. 2015;14:387–404.
  25. Pires DEV, Blundell TL. pkCSM ADMET prediction. Journal of Medicinal Chemistry. 2015;58:4066–4072.
  26. Ghose AK, Viswanadhan VN. Drug-likeness filters. Journal of Combinatorial Chemistry. 1999;1:55–68.
  27. Egan WJ, Merz KM. Prediction of absorption. Journal of Medicinal Chemistry. 2000;43:3867–3877.
  28. Congreve M, Carr R. Fragment-based drug discovery. Drug Discovery Today. 2003;8:876–877.
  29. Spasov AA, Yozhitsa IN. Benzimidazole pharmacology. Pharmaceutical Chemistry Journal. 1999;33:232–243.
  30. Bansal Y, Silakari O. Benzimidazole derivatives in medicinal chemistry. Bioorganic & Medicinal Chemistry.           2012;20:6208–6236.
  31. Gupta S, Kapoor V. Antimicrobial benzimidazoles. Medicinal Chemistry Research. 2014;23:307–321.
  32. Fischer E. Lock and key model. Berichte der Deutschen Chemischen Gesellschaft. 1894.
  33. Koshland DE. Induced fit theory. Proceedings of the National Academy of Sciences. 1958;44:98–104.
  34. Goodsell DS, Olson AJ. Binding interactions. Structure. 1990;1:195–201.
  35. Sliwoski G, Kothiwale S. Computational drug discovery. Pharmacological Reviews. 2014;66:334–395.
  36. Danel T, Podlewska S. Docking-based drug discovery. Journal of Chemical Information and Modeling. 2023.
  37. Banerjee P, Eckert AO. ProTox-II toxicity prediction. Nucleic Acids Research. 2018;46:W257–W263.
  38. Wu Z, Lei T. ADMET prediction tools. Journal of Cheminformatics. 2018;10:1–11.
  39. Cheng F, Li W. Toxicity prediction models. Journal of Chemical Information and Modeling. 2012;52:3099–3105.
  40. Patrick GL. An Introduction to Medicinal Chemistry. 5th ed.
  41. Silverman RB. The Organic Chemistry of Drug Design.
  42. Wermuth CG. The Practice of Medicinal Chemistry.
  43. Zhang S, Yan Z. ADMET prediction using machine learning. Journal of Chemical Information and Modeling. 2022.
  44. Vardhan S. Docking study for SARS-CoV-2. Bioinformatics. 2020.
  45. Abdolmaleki A. QSAR and docking approaches. Current Drug Targets. 2017.
  46.  Lionta E. Drug design strategies. Current Topics in Medicinal Chemistry. 2014.
  47. Ferreira LG. Docking and virtual screening. Molecules. 2015.
  48. Pagadala NS. Docking tools review. Biophysical Reviews. 2017.
  49. Kirchmair J. Drug metabolism prediction. Nature Reviews Drug Discovery. 2015.
  50. Di L. ADMET and drug design principles. Current Opinion in Chemical Biolo

Photo
Mansi Barde
Corresponding author

Pravara Rural College of Pharmacy, Loni, Maharashtra, India- 413736

Photo
Shweta Bairagi
Co-author

Pravara Rural College of Pharmacy, Loni, Maharashtra, India- 413736

Photo
Chetan Barmade
Co-author

Pravara Rural College of Pharmacy, Loni, Maharashtra, India- 413736

Photo
Gaurav Tambe
Co-author

Pravara Rural College of Pharmacy, Loni, Maharashtra, India- 413736

Photo
Amol Dighe
Co-author

Pravara Rural College of Pharmacy, Loni, Maharashtra, India- 413736

Mansi Barde*, Amol Dighe, Chetan Barmade, Shweta Bairagi, Gaurav Tambe, Vehicle Black Box System, Int. J. Sci. R. Tech., 2026, 3 (5), 760-775. https://doi.org/10.5281/zenodo.20325293

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