Pharmacological investigation of formyl and acetyl substituents on piperazine: Docking, ADMET and Molecular Dynamics Simulations

Abstract

Piperazine-based frameworks are central to the development of many modern therapeutics. Structural modifications on this scaffold influence pharmacological behavior. This study examines the pharmacological influence of formyl and acetyl substituents on the piperazine scaffold. Accordingly, 1-formyl and 1-acetyl piperazine, among the smallest biologically active piperazine derivatives, were selected as model compounds, and their binding potentials and drug-likeness properties were evaluated using molecular docking, in silico ADMET analyses, and molecular dynamics simulations. The interactions of the compounds were investigated with a set of biologically relevant targets (tubulin, E. coli MurB enzyme, serotonin 5-HT3 receptor and human histamine H1 receptor) to explore how formyl and acetyl substituents may affect interaction patterns across different protein environments. The results indicate that formyl and acetyl groups can modulate interaction patterns and pharmacokinetic-related properties of the piperazine scaffold. These findings provide a computational basis for the role of simple functional group modifications on piperazine derivatives.

Keywords

• Piperazines
• ADMET
• Docking
• Pharmaceutical potential
• Molecular Dynamics Simulations

Thanks

The authors gratefully acknowledge the High Performance and Grid Computing Center (TRUBA), supported by the Scientific and Technological Research Council of Turkiye (TÜBİTAK), for providing the computational resources used in this study.

References

1. Abdolmaleki, A., Shiri, F., & Ghasemi, J.B. (2021). Chapter 11, Editor(s): M.S. Coumar, Molecular Docking for Computer-Aided Drug Design, Academic Press.
2. Amul, B., Vetrivelan, V., Sakthivel, S., Jeelani, A., Geetha, E. (2024). Structural, spectral analysis, reactivity, topological and pharmacological investigations on an antihistamine agent. Chemical Physics Impact, 8, 100600. https://doi.org/10.1016/j.chphi.2024.100600
3. Bağlayan, Ö., Parlak, C., & Alver, Ö. (2025). Effect of optimization sequence on the Structural and electronic properties of impurity added C20 fullerene: a DFT assessment. Eskişehir Technical University Journal of Science and Technology B- Theoretical Sciences, 11, 35–42. https://doi.org/10.20290/estubtdb.1565576
4. Berman, H.M., Westbrook, J., Feng, Z., Gilliland, G., Bhat, T.N., Weissig, H., Shindyalov, I.N., & Bourne, P.E. (2000). The Protein Data Bank, Nucleic Acids Research, 28, 235–242. https://doi.org/10.1093/nar/28.1.235
5. BIOVIA. (2021). Discovery Studio Visualizer (Version 21.1.0) [Computer software]. BIOVIA.
6. Brenk, R., Schipani, A., James, D., Krasowski, A., Gilbert, I.H., Frearson, J., Wyatt, P.G. (2008). Lessons learnt from assembling screening libraries for drug discovery for neglected diseases. Journal of Chemical Information and Modeling, 48, 1349–1358. https://doi.org/10.1021/ci800047v
7. Champiat, S., Ouali, K., Laparra, A., Charalambous, A., Di Palma, M., Jordan, K., Massard, C., Aapro, M., & Scotte, F. (2025). From toxicity assessment to adaptive safety care: implementing comprehensive fast-track safety evaluation for anticancer drug development. ESMO Open, 10(10), 105796. https://doi.org/10.1016/j.esmoop.2025.105796
8. Chen, Y., & Kirchmair, J. (2020). Cheminformatics in natural product-based drug discovery. Molecular Informatics, 39(12), 2000171. https://doi.org/10.1002/minf.202000171

9. Cheng, F., Yu, Y., Shen, J., Yang, L., Li, W., Liu, G., Lee, P.W., & Tang, Y. (2011). Classification of cytochrome P450 inhibitors and noninhibitors using combined classifiers. Journal of Chemical Information and Modeling, 51(5), 996–1011. https://doi.org/10.1021/ci200028n
10. Cheng, F., Li, W., Zhou, Y., Shen, J., Wu, Z., Liu, G., Lee, P.W., & Tang, Y. (2012). admetSAR: a comprehensive source and free tool for evaluating chemical ADMET properties. Journal of Chemical Information and Modeling, 52, 3099-3105. https://doi.org/10.1021/ci300367a
11. Chopra, R., Bard, J., Svenson, K., & Mansour, T. (2007). Crystal Structure of E. Coli Mur B bound to a Naphthyl Tetronic Acid inihibitor, The Protein Data Bank. https://doi.org/10.2210/pdb2Q85/pdb
12. Crampon, K., Giorkallos, A., Deldossi, M., Baud, S., & Steffenel, L.A. (2022). Machine-learning methods for ligand-protein molecular docking. Drug Discovery Today, 27, 151–164. https://doi.org/10.1016/j.drudis.2021.09.007
13. Daboe, M., Parlak, C., Direm, A., Alver, Ö., & Ramasami, P. (2024). Interaction between escitalopram and ibuprofen or paracetamol: DFT and molecular docking on the drug–drug interactions. Journal of Biomolecular Structure and Dynamics, 42, 672 686. https://doi.org/10.1080/07391102.2023.2195004
14. Daina, A., Michielin, O., & Zoete, V. (2017). SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Scientific Reports, 7, 1–13. https://doi.org/10.1038/srep42717
15. Dwivedi, A.K., Shukla, V.K., Maikhuri, J.P., Srivastava, A., Setty, B.S., & Khanna, N.M. (1991). Synthesis of some 1-formylpiperazine derivatives and sulfaselazine analogs as potential male antifertility agents. Indian Journal of Pharmaceutical Sciences, 53, 170–175.
16. Emir, N., Bilge, M., Tursun, M., Keşan, G., & Parlak, C. (2014). A joint theoretical and experimental study of 1-acetylpiperazine: Conformational stability, infrared and Raman spectra. Spectrochimica Acta A, 127, 388–395. https://doi.org/10.1016/j.saa.2014.02.085
17. Ferrarini, P.L., Mori, C., Badawneh, M., Calderoneb, V., Calzolari, L., Loffredob, T., Martinottib, E., & Saccomannia, G. (1998). Synthesis of l,8 naphthyridine derivatives: Potential antihypertensive agents – Part VII. European Journal of Medicinal Chemistry, 33, 383 397. https://doi.org/10.1016/S0223-5234(98)80014-7
18. Follmann, W., Degen, G., Oesch, F., & Hengstler, J.G. (2013). Ames test. Brenner’s Encyclopedia of Genetics, 2nd ed, Academic Press.
19. Gouedarda, C., Picqa, D., Launayc, F., & Carrettea, P.L. (2012). Amine degradation in CO2 capture. I. A review. International Journal of Greenhouse Gas Control, 10, 244 270. https://doi.org/10.1016/j.ijggc.2012.06.015
20. Hansen, K., Mika, S., Schroeter, T., Sutter, A., ter Laak, A., Steger-Hartmann, T., Heinrich, N., & Müller, K.R. (2009). Benchmark Data Set for in Silico Prediction of Ames Mutagenicity. Journal of Chemical Information and Modeling, 49, 2077-2081. https://doi.org/10.1021/ci900161g
21. Haroun, M., Tratrat, C., Petrou, A., Geronikaki, A., Ivanov, M., Ciric, A., Sokovic, M. (2021). 2-Aryl-3-(6-trifluoromethoxy)benzo[d]thiazole-based thiazolidinone hybrids as potential anti-infective agents: Synthesis, biological evaluation and molecular docking studies. Bioorganic & Medicinal Chemistry Letters, 32, 127718. https://doi.org/10.1016/j.bmcl.2020.127718
22. Hitchcock, S.A. & Pennington, L.D. (2006). Structure-brain exposure relationships. Journal of Medicinal Chemistry, 49, 7559–7583. https://doi.org/10.1021/jm060642i
23. Janicka, M., Sztanke, M., & Sztanke, K. (2024) Modeling the blood-brain barrier permeability of potential heterocyclic drugs via biomimetic IAM chromatography technique combined with QSAR methodology. Molecules, 29(2), 287. https://doi.org/10.3390/molecules29020287
24. Keşan, G., & Parlak, C. (2014). Infrared and Raman spectra, conformational stability and vibrational assignment of 1 formylpiperazine. Spectrochimica Acta A, 118, 1113 1120. https://doi.org/10.1016/j.saa.2013.09.095
25. Khan, S., Sarfraz, A., Prakash, O., Khan, F. (2024). Machine learning-based QSAR modeling, molecular docking, dynamics simulation studies for cytotoxicity prediction in MDA-MB231 triple-negative breast cancer cell line. Journal of Molecular Structure, 1315, 138807. https://doi.org/10.1016/j.molstruc.2024.138807
26. Kumar, A., Srivastava, K., Kumar, S.R., Puri, S.K., & Chauhan, P.M.S. (2008). Synthesis and bioevaluation of hybrid 4-aminoquinoline triazines as a new class of antimalarial agents. Bioorganic & Medicinal Chemistry Letters, 18 6530–6533. https://doi.org/10.1016/j.bmcl.2008.10.049
27. Lagorce, D., Douguet, D., Miteva, M.A., & Villoutreix, B.O. (2017). Computational analysis of calculated physicochemical and ADMET properties of protein-protein interaction inhibitors. Scientific Reports-Uk, 7, 1–15. https://doi.org/10.1038/srep46277
28. Lee, B.S., Chu, S., Lee, B.C., Chi, D.Y., Choe, Y.S., Jeong, K.J., & Jin, C. (2000). Syntheses and binding affinities of 6-nitroquipazine analogues for serotonin transporter. Part 1. Bioorganic & Medicinal Chemistry Letters, 10, 1559–1562. https://doi.org/10.1016/S0960-894X(00)00290-0
29. Li, X., Chen, L., Cheng, F., Wu, Z., Bian, H., Xu, C., Li, W., Liu, G., Shen, X., & Tang, Y. (2014). In silico prediction of chemical acute oral toxicity using multi-classification methods. Journal of Chemical Information and Modeling, 54(4) 1061–1069. https://doi.org/10.1021/ci5000467
30. Li, X., Du, Z., Wang, J., Wu, Z., Li, W., Liu, G., Shen, X., & Tang, Y. (2015). In silico estimation of chemical carcinogenicity with binary and ternary classification methods. Molecular Informatics, 34(4), 228–235. https://doi.org/10.1002/minf.201400127
31. Lindorff-Larsen, K., Piana, S., Palmo, K., Maragakis, P., Klepeis, J.L., Dror, R.O., Shaw, D.E. (2010). Improved side-chain torsion potentials for the Amber ff99SB protein force field. Proteins, 78, 1950–1958. https://doi.org/10.1002/prot.22711
32. Ma, S., McGann, M., & Enyedy, I.J. (2021). The influence of calculated physicochemical properties of compounds on their ADMET profiles. Bioorganic & Medicinal Chemistry Letters, 36, 127825. https://doi.org/10.1016/j.bmcl.2021.127825
33. Mao, Z.W., Zheng, X., Lin, Y.P., Hu, C.Y., Wang, X.L., Wan, C.P., & Rao, G.X. (2016). Design, synthesis and anticancer activity of novel hybrid compounds between benzofuran and N-aryl piperazine, Bioorganic & Medicinal Chemistry Letters, 26, 3421 3424. https://doi.org/10.1016/j.bmcl.2016.06.055
34. Moisescu-Goia, C., Muresan-Pop, M., & Simon, V. (2017). New solid state forms of antineoplastic 5-fluorouracil with anthelmintic piperazine. Journal of Molecular Structure, 1150, 37–43. https://doi.org/10.1016/j.molstruc.2017.08.076
35. Morris, G.M. Huey, R., Lindstrom, W., Sanner, M.F., Belew, R.K., Goodsell, D.S., & Olson, A. J. (2009). AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. Journal of Computational Chemistry, 16, 2785–2791. https://doi.org/10.1002/jcc.21256
36. Muir, B.W., Barden, M.C., Collett, S.P., Gorse, A.D., Monteiro, R., Yang, L., McDougall, N.A., Gould, S., & Maeji, N.J. (2007). High-throughput optimization of surfaces for antibody immobilization using metal complexes. Analytical Biochemistry, 363, 97 107. https://doi.org/10.1016/j.ab.2007.01.015
37. Ononamadu, C.J. & Ibrahim, A. (2021). Molecular docking and prediction of ADME/drug-likeness properties of potentially active antidiabetic compounds isolated from aqueous-methanol extracts of Gymnema sylvestre and Combretum micranthum. Biotechnologia, 102(1), 85 99. http://doi.org/10.5114/bta.2021.103765
38. Pajouhesh, H., & Lenz, G.R. (2005). Medicinal chemical properties of successful central nervous system drugs. NeuroRx: The Journal of the American Society for Experimental NeuroTherapeutics, 2(4), 541–553. https://doi.org/10.1602/neurorx.2.4.541
39. Parlak, C., Alver, Ö., & Bağlayan, Ö. (2021). Quantum mechanical simulation of Molnupiravir drug interaction with Si-doped C60 fullerene. Computational and Theoretical Chemistry, 1202, 113336. https://doi.org/10.1016/j.comptc.2021.113336
40. Patil, V.M., Gaurav, A., & Garg, P. (2021). Non-cancer to anti-cancer: investigation of human ether-a-go-go-related gene potassium channel inhibitors as potential therapeutics. Journal of the Egyptian National Cancer Institute, 33, 33. https://doi.org/10.1186/s43046-021-00091-3
41. Polovinkin, L., Hassaine, G., Perot, J., Neumann, E., Jensen, A.A., Lefebvre, S.N., Corringer, P.J., Neyton, J., Chipot, C., Dehez, F., Schoehn, G., & Nury. H. (2018). Conformational transitions of the serotonin 5-HT3 receptor. Nature, 563(7730), 275–279. https://doi.org/10.1038/s41586-018-0672-3
42. Potts, R.O., & Guy, R.H. (1992). Predicting skin permeability. Pharmaceutical Research, 9(5) 663–669. https://doi.org/10.1023/A:1015810312465
43. Prasanthi, R., Sribalan, R., & Ravichandran, C.T. (2026). Synthesis of piperazine carboxamide substituted vanillin; theoretical and experimental investigation of biological activities, Journal of Molecular Structure, 1354, 144788. https://doi.org/10.1016/j.molstruc.2025.144788
44. Prota, A.E., Danel, F., Bachmann, F., Bargsten, K., Buey, R.M., Pohlmann, J., Reinelt, S., Lane, H., & Steinmetz, M.O. (2014). The Novel Microtubule-Destabilizing Drug BAL27862 Binds to the Colchicine Site of Tubulin with Distinct Effects on Microtubule Organization. Journal of Molecular Biology, 426, 1848-1860. https://doi.org/10.1016/j.jmb.2014.02.005
45. Rai, M., Singh, A.V., Paudel, N., Kanase, A., Falletta, E., Kerkar, P., Heyda, J., Barghash, R.F., Singh, S.P., & Soos, M. (2023). Herbal concoction unveiled: a computational analysis of phytochemicals’ pharmacokinetic and toxicological profiles using novel approach methodologies (NAMs). Current Research in Toxicology, 5, 100118. https://doi.org/10.1016/j.crtox.2023.100118
46. Rezaei-Sameti, M., Rahmenai, F. & Satari, M. (2025). Exploring the molecular interactions between Busulfan anticancer drug and DNA/RNA nucleobases: A comprehensive computational analysis utilizing DFT, TD-DFT, AIM, RDG, solvent effect, and docking techniques. Journal of Molecular Liquids, 425, 127175. https://doi.org/10.1016/j.molliq.2025.12717
47. Rohde, K.H., Michaels, H.A., & Nefzi, A. (2016). Synthesis and antitubercular activity of 1,2,4-trisubstitued piperazines, Bioorganic & Medicinal Chemistry Letters, 26, 2206–2209. https://doi.org/10.1016/j.bmcl.2016.03.063
48. Savjani, K.T., Gajjar, A.K., & Savjani, J.K. (2012). Drug solubility: importance and enhancement techniques. ISRN Pharmaceutics, 1–10. https://doi.org/10.5402/2012/195727
49. Shaquiquzzaman, M., Verma, G., Marella, A., Akhter, M., Akhtar, W., Khan, M.F., Tasneem, S., & Alam, M.M. (2015). Piperazine scaffold: A remarkable tool in generation of diverse pharmacological agents, European Journal of Medicinal Chemistry, 102, 487 529. https://doi.org/10.1016/j.ejmech.2015.07.026
50. Shimamura, T., Shiroishi, M., Weyand, S., Tsujimoto, H., Winter, G., Katritch, V., Abagyan, R., Cherezov, V., Liu, W., Han, G.W., Kobayashi, T., Stevens, R.C., & Iwata, S. (2011). Structure of the human histamine H1 receptor complex with doxepin. Nature, 475, 65-70. https://doi.org/10.1038/nature10236
51. Silva, D.P.B., Florentino, I.F., Oliveira, L.P., Lino, R.C., Galdino, P.M., Menegatti, R., & Costa, E.A. (2015). Anti-nociceptive and anti-inflammatory activities of 4-[(1-phenyl-1H-pyrazol-4-yl) methyl] 1-piperazine carboxylic acid ethyl ester: A new piperazine derivative, Pharmacology Biochemistry and Behavior, 137, 86–92. https://doi.org/10.1016/j.pbb.2015.08.008
52. Simons, F.E., Simons, K.J. (2015). Why are second generation H₁ antihistamines minimally sedating? Journal of Allergy and Clinical Immunology, 135, 386 392. https://pubmed.ncbi.nlm.nih.gov/26291661
53. Sousa da Silva, A. W., Vranken, W. F. (2012). ACPYPE - AnteChamber PYthon Parser interfacE. BMC research notes, 5, 367. https://doi.org/10.1186/1756-0500-5-367
54. Stanzione, F., Giangreco, I., & Cole, J.C. (2021). Use of molecular docking computational tools in drug discovery. Progress in Medicinal Chemistry, 60, 273 343. https://doi.org/10.1016/bs.pmch.2021.01.004
55. Stingaci, E., Zveaghinteva, M., Pogrebnoi, S., Lupascu, L., Valica, V., Uncu, L., Smetanscaia, A., Drumea, M., Petrou, A., Ciric, A., Glamoclija, J., Sokovic, M., Kravtsov, V., Geronikaki, A., Macaev, F. (2020). New vinyl-1,2,4-triazole derivatives as antimicrobial agents: Synthesis, biological evaluation and molecular docking studies, Bioorganic & Medicinal Chemistry Letters, 30, 127368. https://doi.org/10.1016/j.bmcl.2020.127368
56. Tsaioun, K. (2016). Evidence-based absorption, distribution, metabolism, excretion (ADME) and its interplay with alternative toxicity methods. ALTEX, 33, 343 358. https://doi.org/10.14573/altex.1610101
57. Valdés-Tresanco, M.S., Valdés-Tresanco, M.E., Valiente, P.A., Moreno, E. (2021). gmx_MMPBSA: A New Tool to Perform End-State Free Energy Calculations with GROMACS. Journal of Chemical Theory and Computation, 17, 6281–6291. https://doi.org/10.1021/acs.jctc.1c00645
58. Van der Spoel, D., Lindahl, E., Hess, B., Groenhof, G., Mark, A.E., Berendsen, H.J. (2005). GROMACS: fast, flexible, and free. Journal of Computational Chemistry, 26, 1701–1718. https://doi.org/10.1002/jcc.20291
59. Veber, D.F., Johnson, S.R. Cheng, H.Y., Smith, B.R., Ward, K.W., & Kopple, K.D. (2002). Molecular properties that influence the oral bioavailability of drug candidates. Journal of Medicinal Chemistry, 45, 2615–2623. https://doi.org/10.1021/jm020017n
60. Wager, T.T., Chandrasekaran, R.Y., Hou, X., Troutman, M.D., Verhoest, P.R., Villalobos, A., & Will, Y. (2010). Defining desirable central nervous system drug space through the alignment of molecular properties, in vitro ADME, and safety attributes, ACS Chemical Neuroscience, 1, 420–434. https://doi.org/10.1021/cn100007x
61. Wang, Y., Ai, J., Wang, Y., Chen, Y., Wang, L., Liu, G., Geng, M., & Zhang, A. (2011). Synthesis and c-Met Kinase Inhibition of 3,5-Disubstituted and 3,5,7-Trisubstituted Quinolines: Identification of 3-(4-Acetylpiperazin-1-yl)-5-(3-nitrobenzylamino)-7- (trifluoromethyl)quinoline as a Novel Anticancer Agent. Journal of Medicinal Chemistry, 54(7), 2127 2142. https://doi.org/10.1021/jm101340q
62. Wang, S., Li, Y., Wang, J., Chen, L., Zhang, L., Yu, H., & Hou, T. (2012). ADMET evaluation in drug discovery. 12. Development of binary classification models for prediction of hERG potassium channel blockage. Molecular Pharmaceutics, 9(4), 996–1010. https://doi.org/10.1021/mp300023x
63. Williams, J.A., Hyland, R., Jones, B.C., Smith, D.A., Hurst, S., Goosen, T.C., Peterkin, V., Koup, J.R., & Ball, S.E. (2004). Drug-drug interactions for UDP-glucuronosyltransferase substrates: a pharmacokinetic explanation for typically observed low exposure (AUCi/AUC) ratios. Drug Metabolism and Disposition, 32(11), 1201–1208. https://doi.org/10.1124/dmd.104.000794