Data-Driven Derivation of Molecular Substructures That Enhance Drug Activity in Gram-Negative Bacteria
| Autor: | Dominik Gurvič, Andrew Leach, Ulrich Zachariae |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
Drug Development
Gram-Negative Bacterial Infections/drug therapy Drug Resistance Multiple Bacterial Drug Discovery Gram-Negative Bacteria Molecular Medicine Anti-Bacterial Agents/chemistry Humans Microbial Sensitivity Tests Gram-Negative Bacterial Infections Gram-Positive Bacteria Anti-Bacterial Agents |
| Zdroj: | Gurvic, D, Leach, A G & Zachariae, U 2022, ' Data-Driven Derivation of Molecular Substructures That Enhance Drug Activity in Gram-Negative Bacteria ', Journal of Medicinal Chemistry, vol. 65, no. 8, pp. 6088-6099 . https://doi.org/10.1021/acs.jmedchem.1c01984 |
| DOI: | 10.1021/acs.jmedchem.1c01984 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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