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

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.