Dahdouh, EliasAllander, LisaFalgenhauer, LindaIorga, Bogdan ILorenzetti, StefanoMarcos-Alcalde, ÍñigoMartin, Nathaniel IMartinez-Martinez, LuisMingorance, JesusNaas, ThierryRubin, Joseph ESpyrakis, FrancescaTängden, ThomasGomez-Puertas, Paulino2023-05-032023-05-032022-08-27Dahdouh E, Allander L, Falgenhauer L, Iorga BI, Lorenzetti S, Marcos-Alcalde Í, et al. Computational Modeling and Design of New Inhibitors of Carbapenemases: A Discussion from the EPIC Alliance Network. Int J Mol Sci. 2022 Aug 28;23(17):9746http://hdl.handle.net/10668/21191The EPIC consortium brings together experts from a wide range of fields that include clinical, molecular and basic microbiology, infectious diseases, computational biology and chemistry, drug discovery and design, bioinformatics, biochemistry, biophysics, pharmacology, toxicology, veterinary sciences, environmental sciences, and epidemiology. The main question to be answered by the EPIC alliance is the following: "What is the best approach for data mining on carbapenemase inhibitors and how to translate this data into experiments?" From this forum, we propose that the scientific community think up new strategies to be followed for the discovery of new carbapenemase inhibitors, so that this process is efficient and capable of providing results in the shortest possible time and within acceptable time and economic costs.enAttribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/Approach rationalizationInhibitors of carbapenemasesVirtual screeningBacterial proteinsComputational biologyComputer simulationbeta-lactamasesresearch article36077146open accessBiología computacionalProteínas bacterianasSimulación por computadorbeta-lactamasas10.3390/ijms231797461422-0067PMC9456441https://www.mdpi.com/1422-0067/23/17/9746/pdf?version=1661758322https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9456441/pdf