In-silico molecular modelling studies of some camphor imine based compounds as anti-influenza A (H1N1) pdm09 virus agents

References

• Abdullahi, M., Adeniji, S. E., Arthur, D. E., & Musa, S. (2020). Quantitative structure-activity relationship (QSAR) modelling study of some novel carboxamide series as new anti-tubercular agents. Bulletin of the National Research Centre, 44(1), 1–13. https://doi.org/10.1186/s42269-020-00389-7
   Google Scholar
• Abdullahi, M., Das, N., Adeniji, S. E., Usman, A. K., & Sani, A. M. (2021). In-silico design and ADMET predictions of some new imidazo [1, 2-a] pyridine-3-carboxamides (IPAs) as anti-tubercular agents. Journal of Clinical Tuberculosis and Other Mycobacterial Diseases, 25, 100276. https://doi.org/10.1016/j.jctube.2021.100276
   PubMedGoogle Scholar
• Abdullahi, M., Shallangwa, G. A., & Uzairu, A. (2020). In silico QSAR and molecular docking simulation of some novel aryl sulfonamide derivatives as inhibitors of H5N1 influenza A virus subtype. Beni-Suef University Journal of Basic and Applied Sciences, 9(1), 1–12. https://doi.org/10.1186/s43088-019-0023-y
   Google Scholar
• Abdullahi, M., Uzairu, A., Shallangwa, G. A., Arthur, D. E., Umar, B. A., & Ibrahim, M. T. (2020). Virtual molecular docking study of some novel carboxamide series as new anti-tubercular agents. European Journal of Chemistry, 11(1), 30–36. https://doi.org/10.5155/eurjchem.11.1.30-36.1955
   Google Scholar
• Abdullahi, M., Uzairu, A., Shallangwa, G. A., Mamza, P. A., & Ibrahim, M. T. (2023). Computational modelling of some phenolic diterpenoid compounds as anti-influenza A virus agents. Scientific African, 19, e01462. https://doi.org/10.1016/j.sciaf.2022.e01462
   Google Scholar
• Abdullahi, M., Uzairu, A., Shallangwa, G. A., Mamza, P. A., & Ibrahim, M. T. (2022). 2D-QSAR, 3D-QSAR, molecular docking and ADMET prediction studies of some novel 2-((1H-indol-3-yl) thio)-N-phenyl-acetamide derivatives as anti-influenza A virus. Egyptian Journal of Basic and Applied Sciences, 9(1), 510–532. https://doi.org/10.1080/2314808X.2022.2108592
   Google Scholar
• Abdullahi, M., Uzairu, A., Shallangwa, G. A., Mamza, P. A., & Ibrahim, M. T. (2022). Computational modelling studies of some 1, 3-thiazine derivatives as anti-influenza inhibitors targeting H1N1 neuraminidase via 2D-QSAR, 3D-QSAR, molecular docking, and ADMET predictions. Beni-Suef University Journal of Basic and Applied Sciences, 11(1), 1–22. https://doi.org/10.1186/s43088-022-00280-6
   Google Scholar
• Ahamad, S., Islam, A., Ahmad, F., Dwivedi, N., & Hassan, M. I. (2019). 2/3D-QSAR, molecular docking and MD simulation studies of FtsZ protein targeting benzimidazoles derivatives. Computational Biology and Chemistry, 78, 398–413. https://doi.org/10.1016/j.compbiolchem.2018.12.017
   PubMed Web of Science ®Google Scholar
• Ahmed, A., Saeed, A., Ejaz, S. A., Aziz, M., Hashmi, M. Z., Channar, P. A., Abbas, Q., Raza, H., Shafiq, Z., & El-Seedi, H. R. (2022). Novel adamantyl clubbed iminothiazolidinones as promising elastase inhibitors: Design, synthesis, molecular docking, ADMET and DFT studies. RSC Advances, 12(19), 11974–11991. https://doi.org/10.1039/d1ra09318e
   PubMed Web of Science ®Google Scholar
• Al-Attraqchi, O. H. A., & Venugopala, K. N. (2020). 2D- and 3D-QSAR modeling of imidazole-based glutaminyl cyclase inhibitors. Current Computer-Aided Drug Design, 16(6), 682–697. https://doi.org/10.2174/1573409915666190918150136
   PubMed Web of Science ®Google Scholar
• Al-Otaibi, J. S., Mary, Y. S., Mary, S., Trivedi, R., Chakraborty, B., Yadav, R., Celik, I., & Soman, S. (2022). DFT and MD investigations of the biomolecules of phenothiazine derivatives: Interactions with gold and water molecules and investigations in search of effective drug for SARS-CoV-2. Journal of Biomolecular Structure and Dynamics, 1–12. https://doi.org/10.1080/07391102.2022.2068649
   Web of Science ®Google Scholar
• Aouidate, A., Ghaleb, A., Ghamali, M., Chtita, S., Ousaa, A., Choukrad, M., Sbai, A., Bouachrine, M., & Lakhlifi, T. (2018). Computer aided drug design based on 3D-QSAR and molecular docking studies of 5-(1H-indol-5-yl)-1,3,4-thiadiazol-2-amine derivatives as PIM2 inhibitors: A proposal to chemists. In Silico Pharmacology, 6(1), 5. https://doi.org/10.1007/s40203-018-0043-7
   PubMedGoogle Scholar
• Arthur, D. E., Samuel, A. N., Ejeh, S., Adeniji, S. E., Adedirin, O., & Abdullahi, M. (2020). Computational study of some cancer drugs as potent inhibitors of GSK3β. Scientific African, 10, e00612. https://doi.org/10.1016/j.sciaf.2020.e00612
   Google Scholar
• Avila, G., Cruz-Licea, V., Rojas-Espinosa, K., Bermúdez-Álvarez, Y., Grostieta, E., Romero-Valdovinos, M., Martínez-Hernández, F., Vaughan, G., & Flisser, A. (2020). Influenza A H1N1 Virus 2009 synthetic hemagglutinin and neuraminidase peptides for antibody detection. Archives of Medical Research, 51(5), 436–443. https://doi.org/10.1016/j.arcmed.2020.04.011
   PubMed Web of Science ®Google Scholar
• Aziz, M., Ejaz, S. A., Tamam, N., Siddique, F., Riaz, N., Qais, F. A., Chtita, S., & Iqbal, J. (2022). Identification of potent inhibitors of NEK7 protein using a comprehensive computational approach. Scientific Reports, 12(1), 1–17. https://doi.org/10.1038/s41598-022-10253-5
   PubMed Web of Science ®Google Scholar
• Bhadresha, K., Kumar, S. P., Brahmbhatt, J., Patel, C., Pandya, P., Jain, N., & Rawal, R. (2022). Theaflavin-3-gallate, a natural antagonist for Hsp90: In-silico and in-vitro approach. Chemico-Biological Interactions, 353, 109774. https://doi.org/10.1016/j.cbi.2021.109774
   PubMed Web of Science ®Google Scholar
• Bouakkadia, A., Kertiou, N., Amiri, R., Driouche, Y., & Messadi, D. (2021). Use of GA-ANN and GA-SVM for a QSPR study on the aqueous solubility of pesticides. Journal of the Serbian Chemical Society, 86(7–8), 673–684. https://doi.org/10.2298/JSC200618066B
   Web of Science ®Google Scholar
• Boukabcha, N., Benmohammed, A., Belhachemi, M. H. M., Goudjil, M., Yahiaoui, S., Megrouss, Y., Djafri, A., Khelloul, N., Benyehlou, Z. D., Djafri, A., & Chouaih, A. (2023). Spectral investigation, TD-DFT study, Hirshfeld Surface analysis, NCI-RDG, HOMO-LUMO, chemical reactivity and NLO properties of 1-(4-fluorobenzyl)-5-bromolindolin-2, 3-dione. Journal of Molecular Structure, 1285, 135492. https://doi.org/10.1016/j.molstruc.2023.135492
   Web of Science ®Google Scholar
• Brunt, D., Lakernick, P. M., & Wu, C. (2022). Discovering new potential inhibitors to SARS-CoV-2 RNA dependent RNA polymerase (RdRp) using high throughput virtual screening and molecular dynamics simulations. Scientific Reports, 12(1), 1–19. https://doi.org/10.1038/s41598-022-24695-4
   PubMed Web of Science ®Google Scholar
• Charanya, C., Sampathkrishnan, S., Kumutha, R., Praveena, J., Bhaskaran, A., Prabakaran, A., & Balamurugan, N. (2023). Synthesis, quantum computational analysis and molecular docking of 3-(2-hydroxyphenyl)-1-phenyl propanone: A combined experimental and theoretical analysis. Polycyclic Aromatic Compounds, 1–30. https://doi.org/10.1080/10406638.2022.2157025
   Web of Science ®Google Scholar
• Chauhan, K., Singh, P., Kumar, V., Shukla, P. K., Siddiqi, M. I., & Chauhan, P. M. (2014). Investigation of Ugi-4CC derived 1H-tetrazol-5-yl-(aryl) methyl piperazinyl-6-fluoro-4-oxo-1,4-dihydroquinoline-3-carboxylic acid: Synthesis, biology and 3D-QSAR analysis. European Journal of Medicinal Chemistry, 78, 442–454. https://doi.org/10.1016/j.ejmech.2014.03.069
   PubMed Web of Science ®Google Scholar
• Chen, N., Wen, J., Wang, Z., & Wang, J. (2022). Multiple regulation and targeting effects of borneol in the neurovascular unit in neurodegenerative diseases. Basic & Clinical Pharmacology & Toxicology, 130(1), 5–19. https://doi.org/10.1111/bcpt.13656
   PubMed Web of Science ®Google Scholar
• Costantino, A. R., Charbe, N., Duarte, Y., Gutiérrez, M., Giordano, A., Prasher, P., Dua, K., Mandolesi, S., & Zacconi, F. C. (2022). Toward the cholinesterase inhibition potential of TADDOL derivatives: Seminal biological and computational studies. Archiv der Pharmazie, 355(11), 2200142. https://doi.org/10.1002/ardp.202200142
   Web of Science ®Google Scholar
• de Cássia da Silveira e Sá, R., Andrade, L. N., & de Sousa, D. P. (2013). A review on anti-inflammatory activity of monoterpenes. Molecules (Basel, Switzerland), 18(1), 1227–1254. https://doi.org/10.3390/molecules18011227
   PubMed Web of Science ®Google Scholar
• El Aissouq, A., Toufik, H., Lamchouri, F., Stitou, M., Ouammou, A., editors. (2019). QSAR study of isonicotinamides derivatives as Alzheimr’s disease inhibitors using PLS-R and ANN methods [Paper presentation]. 2019 International Conference on Intelligent Systems and Advanced Computing Sciences (ISACS), IEEE. https://doi.org/10.1109/ISACS48493.2019.9068919
   Google Scholar
• Eryildiz, B., Ozgun, H., Ersahin, M. E., & Koyuncu, I. (2022). Antiviral drugs against influenza: Treatment methods, environmental risk assessment and analytical determination. Journal of Environmental Management, 318, 115523. https://doi.org/10.1016/j.jenvman.2022.115523
   PubMed Web of Science ®Google Scholar
• Ghosh, A., Chakraborty, M., Chandra, A., & Alam, M. P. (2021). Structure-activity relationship (SAR) and molecular dynamics study of withaferin-A fragment derivatives as potential therapeutic lead against main protease (Mpro) of SARS-CoV-2. Journal of Molecular Modeling, 27(3), 1–17. https://doi.org/10.1007/s00894-021-04703-6
   Web of Science ®Google Scholar
• Goudzal, A., El Aissouq, A., El Hamdani, H., Hadaji, E. G., Ouammou, A., & Bouachrine, M. (2022). 3D-QSAR modeling and molecular docking studies on a series of 2, 4, 5-trisubstituted imidazole derivatives as CK2 inhibitors. Journal of Biomolecular Structure and Dynamics, 41(1), 234-248. https://doi.org/10.1080/07391102.2021.2014360.
   PubMed Web of Science ®Google Scholar
• Gu, X., Wang, Y., Wang, M., Wang, J., & Li, N. (2021). Computational investigation of imidazopyridine analogs as protein kinase B (Akt1) allosteric inhibitors by using 3D-QSAR, molecular docking and molecular dynamics simulations. Journal of Biomolecular Structure & Dynamics, 39(1), 63–78. https://doi.org/10.1080/07391102.2019.1705185
   PubMed Web of Science ®Google Scholar
• Hasan, A., Sasaki, T., Phadungsombat, J., Koketsu, R., Rahim, R., Ara, N., Biswas, S. M., Yonezawa, R., Nakayama, E. E., Rahman, M., & Shioda, T. (2022). Genetic analysis of Influenza A/H1N1pdm strains isolated in Bangladesh in early 2020. Tropical Medicine and Infectious Disease, 7(3), 38. https://doi.org/10.3390/tropicalmed7030038
   PubMedGoogle Scholar
• Ibrahim, M. T., Uzairu, A., Shallangwa, G. A., & Uba, S. (2020). Structure-based design and activity modeling of novel epidermal growth factor receptor kinase inhibitors; an in silico approach. Scientific African, 9, e00503. https://doi.org/10.1016/j.sciaf.2020.e00503
   Google Scholar
• Janani, S., Rajagopal, H., Muthu, S., Aayisha, S., & Raja, M. (2021). Molecular structure, spectroscopic (FT-IR, FT-Raman, NMR), HOMO-LUMO, chemical reactivity, AIM, ELF, LOL and Molecular docking studies on 1-Benzyl-4-(N-Boc-amino) piperidine. Journal of Molecular Structure, 1230, 129657. https://doi.org/10.1016/j.molstruc.2020.129657
   Web of Science ®Google Scholar
• Kar, S., Roy, K., & Leszczynski, J. (2022). In silico tools and software to predict ADMET of new drug candidates. In Silico methods for predicting drug (pp. 85–115). Springer.
   Google Scholar
• Khalid, M., Shafiq, I., Mahmood, K., Hussain, R., Ur Rehman, M. F., Assiri, M. A., Imran, M., Akram, M. S., & Umm-E-Hani. (2023). Effect of different end-capped donor moieties on non-fullerenes based non-covalently fused-ring derivatives for achieving high-performance NLO properties. Scientific Reports, 13(1), 1395. https://doi.org/10.1038/s41598-023-28118-w
   PubMed Web of Science ®Google Scholar
• Korsten, K., Adriaenssens, N., Coenen, S., Butler, C. C., Verheij, T. J. M., Bont, L. J.,Wildenbeest, J. G., & Investigators, R. (2021). World Health Organization influenza-like illness underestimates the burden of respiratory syncytial virus infection in community-dwelling older adults. Journal of Infectious Diseases, 226(Supplement_1), S71-S78.
   Google Scholar
• Lomartire, S., & Gonçalves, A. M. (2022). Antiviral activity and mechanisms of seaweeds bioactive compounds on enveloped viruses—a review. Marine Drugs, 20(6), 385. https://doi.org/10.3390/md20060385
   PubMed Web of Science ®Google Scholar
• Molski, M. (2023). Theoretical study on the radical scavenging activity of gallic acid. Heliyon, 9(1), e12806. https://doi.org/10.1016/j.heliyon.2023.e12806
   PubMed Web of Science ®Google Scholar
• Patel, D. C., Hausman, K. R., Arba, M., Tran, A., Lakernick, P. M., & Wu, C. (2021). Novel inhibitors to ADP ribose phosphatase of SARS-CoV-2 identified by structure-based high throughput virtual screening and molecular dynamics simulations. Computers in Biology and Medicine, 140, 105084. https://doi.org/10.1016/j.compbiomed.2021.105084
   PubMed Web of Science ®Google Scholar
• Reyes-Chaparro, A., Flores-Lopez, N., Quintanilla-Guerrero, F., Nicolás-Álvarez, D. E., & Hernandez-Martinez, A. (2023). Design of new reversible and selective inhibitors of monoamine oxidase A and a comparison with drugs already approved. Bulletin of the National Research Centre, 47(1), 1–13. https://doi.org/10.1186/s42269-023-01018-9
   Google Scholar
• Roy, K., Das, R. N., Ambure, P., & Aher, R. B. (2016). Be aware of error measures. Further studies on validation of predictive QSAR models. Chemometrics and Intelligent Laboratory Systems, 152, 18–33. https://doi.org/10.1016/j.chemolab.2016.01.008
   Web of Science ®Google Scholar
• Roy, K., Kar, S., & Das, R. N. (2015). Statistical methods in QSAR/QSPR. A primer on QSAR/QSPR modeling (pp. 37–59). Springer.
   Google Scholar
• Sarker, A., Gu, Z., Mao, L., Ge, Y., Hou, D., Fang, J., Wei, Z., & Wang, Z. (2022). Influenza-existing drugs and treatment prospects. European Journal of Medicinal Chemistry, 232, 114189. https://doi.org/10.1016/j.ejmech.2022.114189
   PubMed Web of Science ®Google Scholar
• Satpathy, R., Guru, R. K., & Behera, R. (2010). Computational QSAR analysis of some physiochemical and topological descriptors of Curcumin derivatives by using different statistical methods. Journal of Chemical and Pharmaceutical Research, 2(6), 344–350.
   Google Scholar
• Schrödinger. (2018). Schrödinger Release 2018-4: Desmond Molecular Dynamics System, DE Shaw Research, Maestro-Desmond Interoperability Tools, 2018,
   Google Scholar
• Shaw, D. E., Dror, R. O., Salmon, J. K., Grossman, J., Mackenzie, K. M., Bank, J. A., et al., editors. (2009). Millisecond-scale molecular dynamics simulations on Anton [Paper presentation]. Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis. https://doi.org/10.1145/1654059.1654126
   Google Scholar
• Shokova, E., Kim, J., & Kovalev, V. (2016). Camphor and its derivatives. Unusual transformations and biological activity. Russian Journal of Organic Chemistry, 52(4), 459–488. https://doi.org/10.1134/S1070428016040011
   Web of Science ®Google Scholar
• Sokolova, A. S., Yarovaya, O. I., Baranova, D. V., Galochkina, A. V., Shtro, A. A., Kireeva, M. V., Borisevich, S. S., Gatilov, Y. V., Zarubaev, V. V., & Salakhutdinov, N. F. (2021). Quaternary ammonium salts based on (-)-borneol as effective inhibitors of influenza virus. Archives of Virology, 166(7), 1965–1976. https://doi.org/10.1007/s00705-021-05102-1
   PubMed Web of Science ®Google Scholar
• Sokolova, A. S., Yarovaya, O. I., Shernyukov, A. V., Gatilov, Y. V., Razumova, Y. V., Zarubaev, V. V., Tretiak, T. S., Pokrovsky, A. G., Kiselev, O. I., & Salakhutdinov, N. F. (2015). Discovery of a new class of antiviral compounds: Camphor imine derivatives. European Journal of Medicinal Chemistry, 105, 263–273. https://doi.org/10.1016/j.ejmech.2015.10.010
   PubMed Web of Science ®Google Scholar
• Thompson, C. G., Kim, R. S., Aloe, A. M., & Becker, B. J. (2017). Extracting the variance inflation factor and other multicollinearity diagnostics from typical regression results. Basic and Applied Social Psychology, 39(2), 81–90. https://doi.org/10.1080/01973533.2016.1277529
   Web of Science ®Google Scholar
• Tropsha, A. (2010). Best practices for QSAR model development, validation, and exploitation. Molecular Informatics, 29(6–7), 476–488. https://doi.org/10.1002/minf.201000061
   PubMed Web of Science ®Google Scholar
• Tropsha, A., & Golbraikh, A. (2007). Predictive QSAR modeling workflow, model applicability domains, and virtual screening. Current Pharmaceutical Design, 13(34), 3494–3504. https://doi.org/10.2174/138161207782794257
   PubMed Web of Science ®Google Scholar
• Tropsha, A., Gramatica, P., & Gombar, V. K. (2003). The importance of being earnest: Validation is the absolute essential for successful application and interpretation of QSPR models. QSAR & Combinatorial Science, 22(1), 69–77. https://doi.org/10.1002/qsar.200390007
   Google Scholar
• Umar, A. B., & Uzairu, A. (2023). Virtual screening, pharmacokinetic, and DFT studies of anticancer compounds as potential V600E-BRAF kinaseinhibitors. Journal of Taibah University Medical Sciences, 18(5), 933–946. https://doi.org/10.1016/j.jtumed.2023.01.013
   PubMed Web of Science ®Google Scholar
• Vavricka, C. J., Li, Q., Wu, Y., Qi, J., Wang, M., Liu, Y., Gao, F., Liu, J., Feng, E., He, J., Wang, J., Liu, H., Jiang, H., & Gao, G. F. (2011). Structural and functional analysis of laninamivir and its octanoate prodrug reveals group specific mechanisms for influenza NA inhibition. PLoS Pathogens, 7(10), e1002249. https://doi.org/10.1371/journal.ppat.1002249
   PubMed Web of Science ®Google Scholar
• Vishwakarma, K., & Bhatt, H. (2021). Molecular modelling of quinoline derivatives as telomerase inhibitors through 3D-QSAR, molecular dynamics simulation, and molecular docking techniques. Journal of Molecular Modeling, 27(2), 30. https://doi.org/10.1007/s00894-020-04648-2
   PubMed Web of Science ®Google Scholar
• Wang, T., Tang, L., Luan, F., & Cordeiro, M. (2018). Prediction of the toxicity of binary mixtures by QSAR approach using the hypothetical descriptors. International Journal of Molecular Sciences, 19(11), 3423. https://doi.org/10.3390/ijms19113423
   PubMed Web of Science ®Google Scholar
• Yap, C. W. (2011). PaDEL‐descriptor: An open source software to calculate molecular descriptors and fingerprints. Journal of Computational Chemistry, 32(7), 1466–1474. https://doi.org/10.1002/jcc.21707
   PubMed Web of Science ®Google Scholar
• Zhang, W., Xu, H., Guan, S., Wang, C., & Dong, G. (2022). Frequency and distribution of H1N1 influenza A viruses with oseltamivir‐resistant mutations worldwide before and after the 2009 pandemic. Journal of Medical Virology, 94(9), 4406–4416. https://doi.org/10.1002/jmv.27870
   PubMed Web of Science ®Google Scholar
• Zielińska-Błajet, M., & Feder-Kubis, J. (2020). Monoterpenes and their derivatives—Recent development in biological and medical applications. International Journal of Molecular Sciences, 21(19), 7078. https://doi.org/10.3390/ijms21197078
   PubMed Web of Science ®Google Scholar