Open Access
| Issue |
EPJ Web Conf.
Volume 348, 2026
3rd International Conference on Innovations in Molecular Structure & Instrumental Approaches (ICMSI 2026)
|
|
|---|---|---|
| Article Number | 01027 | |
| Number of page(s) | 34 | |
| Section | Life Science | |
| DOI | https://doi.org/10.1051/epjconf/202634801027 | |
| Published online | 21 January 2026 | |
- A.M. Somboro, J.O. Sekyere, D.G. Amoako, S.Y. Essack, L.A. Bester, Diversity and proliferation of metallo-β-lactamases: a clarion call for clinically effective metallo-β-lactamase inhibitors. Appl. Environ. Microbiol. 84, e00698–18 (2018). [Google Scholar]
- D. Carcione, C. Siracusa, A. Sulejmani, V. Leoni, J. Intra, Old and new beta-lactamase inhibitors: Molecular structure, mechanism of action, and clinical Use. Antibiotics. 10, 995 (2021). [Google Scholar]
- S. Shaikh, J. Fatima, S. Shakil, S.M.D. Rizvi, M.A. Kamal, Antibiotic resistance and extended spectrum beta-lactamases: types, epidemiology and treatment. Saudi J. Biol. Sci. 22, 90–101 (2015). [CrossRef] [Google Scholar]
- M.A. Cook, G.D. Wright, The past, present, and future of antibiotics. Sci. Transl. Med. 14, eabo7793 (2022). [Google Scholar]
- A. Srivastava, M. Kumar, Prediction of zinc binding sites in proteins using sequence derived information. J. Biomol. Struct. Dyn. 36, 4413–4423 (2018). [Google Scholar]
- A. Krajnc et al., Bicyclic boronate VNRX-5133 inhibits metallo- and serine-β-lactamases. J. Med. Chem. (18):8544–8556. (2019). [Google Scholar]
- S. Skagseth, S. Akhter, M.H. Paulsen, Z. Muhammad, S. Lauksund, O. Samuelsen, H.-K.S. Leiros, A. Bayer, Metallo-β-lactamase inhibitors by bioisosteric replacement: Preparation, activity and binding. Eur. J. Med. Chem. 135, 159–173 (2017). [Google Scholar]
- T. Wang, K. Xu, L. Zhao, R. Tong, L. Xiong, J. Shi, Recent research and development of NDM-1 inhibitors. Eur. J. Med. Chem. 223, 113667 (2021). [Google Scholar]
- M. Malabanan et al., A role for flexible loops in enzyme catalysis. Current Opinion in Structural Biology. Curr. Opin. Struct. Biol. 20, 702–710 (2010). [Google Scholar]
- C. Andreini, L. Banci, I. Bertini, A. Rosato, A bioinformatics view of zinc enzymes. J. Inorg. Biochem. 111, 150–156 (2012). [Google Scholar]
- N.M. O'Boyle, M. Banck, C.A. James, C. Morley, T. Vandermeersch, G.R. Hutchison, Open Babel: An open chemical toolbox. J. Cheminform. 3, 33 (2011). [Google Scholar]
- O. Trott, A.J. Olson, AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization and multithreading. J. Comput. Chem. 31, 455–461 (2010). [CrossRef] [PubMed] [Google Scholar]
- S. Kim, P.A. Thiessen, E.E. Bolton et al., PubChem substance and compound databases. Nucleic Acids Res. 44, D1202-D1213 (2016). [Google Scholar]
- R.A. Powers, J. Blázquez, G.S. Weston, M.I. Morosini, F. Baquero, B.K. Shoichet, The complexed structure and antimicrobial activity of a non-beta-lactam inhibitor of AmpC beta lactamase. Protein Sci. 8, 2330–2337 (1999). [Google Scholar]
- P. Banerjee, A.O. Eckert, A.K. Schrey, R. Preissner, ProTox-II: A webserver for the prediction of toxicity of chemicals. Nucleic Acids Res. 46, 257–263 (2018). [Google Scholar]
- R. Rolta, R. Yadav, D. Salaria, S. Trivedi, M. Imran, A. Sourirajan, D.J. Baumler, K. Dev, In silico screening of hundred phytocompounds of ten medicinal plants as potential inhibitors of nucleocapsid phosphoprotein of COVID-19: An approach to prevent virus assembly. J. Biomol. Struct. Dyn. 1–8 (2020). [Google Scholar]
- R. Rolta, D. Salaria, V. Kumar, A. Sourirajan, K. Dev, Phytocompounds of Rheum emodi, Thymus serpyllum and Artemisia annua inhibit COVID-19 binding to ACE2 receptor: In silico approach. Curr. Pharmacol. Rep. 7, 135–149 (2021). [Google Scholar]
- D. Salaria, R. Rolta, N. Sharma, K. Dev, A. Sourirajan, V. Kumar, In silico and In vitro evaluation of the anti-inflammatory and antioxidant potential of Cymbopogon citratus from North-western Himalayas. BioRxiv (2020). [Google Scholar]
- A. Daina, O. Michielin, V. Zoete, SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci. Rep. 7, 42717 (2017). [CrossRef] [Google Scholar]
- F. Cheng, W. Li, Y. Zhou, J. Shen, Z. Wu, G. Liu, P.W. Lee, Y. Tang, AdmetSAR: A comprehensive source and free tool for assessment of chemical ADMET properties. J. Chem. Inf. Model. 52, 3099–3105 (2012). [Google Scholar]
- H. Yang, C. Lou, L. Sun, J. Li, Y. Cai, Z. Wang, Y. Tang, AdmetSAR 2.0: Web-service for prediction and optimization of chemical ADMET properties. Bioinformatics. 35, 1067–1069 (2019). [Google Scholar]
- M. Amin, D. Greenwood, M. Yashar, Emerging resistance mechanisms in carbapenem-resistant Enterobacteriaceae. J. Glob. Antimicrob. Resist. 25, 120–129 (2021). [Google Scholar]
- Z. Bibi, I. Asghar, N.M. Ashraf, I. Zeb, et al., Prediction of phytochemicals for their potential to inhibit New Delhi metallo β-lactamase (NDM-1). Pharmaceuticals. 16, 1404 (2023). [Google Scholar]
- A. Daina, O. Michielin, V. Zoete, SwissADME: A free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci. Rep. 7, 42717 (2017). [CrossRef] [Google Scholar]
- M.A. Salomào, M.C.R. Cordeiro, M.R. Rodrigues, Phytosterols as inhibitors of New Delhi metallo-β-lactamase (NDM-1): An in silico study. Sci. Rep. 14, 3951 (2024). [Google Scholar]
- N. Gupta, R. Singh, Antimicrobial potential of Gymnema sylvestre: A review. J. Tradit. Complement. Med. 9, 165–172 (2019). [Google Scholar]
- Q. Ma et al., Insights into the effects and mechanism of andrographolide in restoring β-lactam antibiotic susceptibility. Microbiol. Spectr. 11, e02978–22 (2023). [Google Scholar]
- A. Müller, L. Brown, Natural product scaffolds in antibiotic discovery: A focused review. Drug Discov. Today 26, 2145–2153 (2021). [Google Scholar]
- K. Singh, Y. Naidoo, H. Baijnath, A comprehensive review on the genus Plumbago with focus on Plumbago auriculata (Plumbaginaceae), Afr. J. Tradit. Complement. Altern. Med. 15, 199–215 (2017). [Google Scholar]
- O. Trott, A.J. Olson, AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J. Comput. Chem. 31, 455–461 (2010). [CrossRef] [PubMed] [Google Scholar]
- X. Li, D. Zhao, W. Li, J. Sun, X. Zhang, Enzyme inhibitors: The best strategy to tackle superbug NDM-1 and its variants. Int. J. Mol. Sci. 23, 197 (2022). [Google Scholar]
- M.N. Drwal, P. Banerjee, M. Dunkel, M.R. Wettig, R. Preissner, ProTox: A web server for the in-silico prediction of toxicity of small molecules. Nucleic Acids Res. 42, W53-W58 (2014). [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.

