PseudoResistance DB: A new Database of antibiotics related to Pseudomonas aeruginosa antibiotic resistance

This research addresses the pressing issue of antibiotic resistance, a global health challenge that undermines the efficacy of treatments against infectious diseases. Focusing on Pseudomonas aeruginosa—a Gram-negative bacterium known for causing opportunistic infections—this study emphasizes its prioritization by the World Health Organization (WHO) as a critical-level pathogen requiring new therapeutic approaches.

To identify antibiotics associated with P. aeruginosa, the study employed text mining techniques on the Scielo database. The resulting dataset comprises 98 antibiotics, each documented with detailed textual information and referencing data. Additionally, the dataset includes structural files of the antibiotics in several formats suitable for computational modeling and simulations. These formats encompass Protein Data Bank, Partial Charge & Atom Type (PDBQT), Simplified Molecular Input Line Entry System (SMI), IUPAC International Chemical Identifier (INCHI), Molecular Design Limited Molfile (MOL2), Structure-Data File (SDF), Chemical Markup Language (CML), Cartesian Coordinates File (XYZ), Scalable Vector Graphics (SVG), Molecular File (MOL) and Protein Data Bank (PDB) files, with molecular models generated via OpenBabel to facilitate advanced studies in drug development and resistance mechanisms.