Data-driven derivation of molecular substructures that enhance drug activity in Gram-negative bacteria

TY - JOUR

T1 - Data-driven derivation of molecular substructures that enhance drug activity in Gram-negative bacteria

AU - Gurvic, Dominik

AU - Leach, Andrew G.

AU - Zachariae, Ulrich

N1 - Funding Information: D.G. and U.Z. were supported by funding from the MRC (iCASE award MR/R015791/1 together with Helperby Ltd.). We thank members of the data analysis group, James Abbott and Marek Gierlinski as well as Sir Anthony Coates, Robert Hammond, and Stephen Gillespie, for fruitful discussions, and David Helekal and Jianguo Zhang for help during the inception of the project. We are grateful to Medchemica Ltd for providing access to MCpairs and to Drs. Al Dossetter and Ed Griffen for support with matched pair generation. Publisher Copyright: © 2022 The Authors. Published by American Chemical Society

PY - 2022/4/28

Y1 - 2022/4/28

N2 - 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.

AB - 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.

KW - Amines

KW - Anions

KW - Bacteria

KW - Molecules

KW - Thiophenes

UR - https://www.scopus.com/pages/publications/85128851992

U2 - 10.1021/acs.jmedchem.1c01984

DO - 10.1021/acs.jmedchem.1c01984

M3 - Article

C2 - 35427114

SN - 0022-2623

VL - 65

SP - 6088

EP - 6099

JO - Journal of Medicinal Chemistry

JF - Journal of Medicinal Chemistry

IS - 8

ER -