Data-Driven Derivation of Molecular Substructures That Enhance Drug Activity in Gram-Negative Bacteria
journal contributionposted on 2022-04-15, 18:07 authored by Dominik Gurvic, Andrew G. Leach, Ulrich Zachariae
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.
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world health organizationresistant bacterial infectionsfindings may helpconsistently improve activitycomplex cell envelopeuncover chemical featuresenhance drug activitynegative bacteria createsnegative bacteriachemical spacenegative uptakenegative permeabilitynegative bioactivitywider rangewide rangespectrum antibioticspreviously knownoptimize compoundsmolecular substructuresmachine learningformidable barrierefficient treatmentsdriven derivationdriven approachdevelop newbetter understandingantibiotic influx