Data-Driven Derivation of Molecular Substructures That Enhance Drug Activity in Gram-Negative Bacteria

Dominik Gurvic, Andrew G. Leach, Ulrich Zachariae

Research output: Contribution to journalArticlepeer-review


The complex cell envelope of Gram-negative bacteria creates a formidable barrier to antibiotic influx. Reduced drug uptake impedes drug development and contributes to a wide range of drug-resistant bacterial infections, including those caused by extremely resistant species prioritized by the World Health Organization. To develop new and efficient treatments, a better understanding of the molecular features governing Gram-negative permeability is essential. Here, we present a data-driven approach, using matched molecular pair analysis and machine learning on minimal inhibitory concentration data from Gram-positive and Gram-negative bacteria to uncover chemical features that influence Gram-negative bioactivity. We find recurring chemical moieties, of a wider range than previously known, that consistently improve activity and suggest that this insight can be used to optimize compounds for increased Gram-negative uptake. Our findings may help to expand the chemical space of broad-spectrum antibiotics and aid the search for new antibiotic compound classes.

Original languageEnglish
Pages (from-to)6088-6099
Number of pages12
JournalJournal of Medicinal Chemistry
Issue number8
Early online date15 Apr 2022
Publication statusPublished - 28 Apr 2022


  • Anti-Bacterial Agents/chemistry
  • Drug Development
  • Drug Resistance, Multiple, Bacterial
  • Gram-Negative Bacteria
  • Gram-Negative Bacterial Infections/drug therapy
  • Gram-Positive Bacteria
  • Humans
  • Microbial Sensitivity Tests


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