Details

Autor(en) / Beteiligte

• Gurvic, Dominik
• Leach, Andrew G
• Zachariae, Ulrich

Titel

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

Ist Teil von

• Journal of medicinal chemistry, 2022-04, Vol.65 (8), p.6088-6099

Ort / Verlag

United States: American Chemical Society

Erscheinungsjahr

2022

Quelle

MEDLINE

Beschreibungen/Notizen

• 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.