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Discovering NDM-1 inhibitors using molecular substructure embeddings representations.

Thomas Papastergiou1,2, Jérôme Azé1, Sandra Bringay1,3

  • 1LIRMM, University of Montpellier, CNRS, 34095 Montpellier, France.

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|July 27, 2023
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Summary
This summary is machine-generated.

A new database and machine learning approach were developed to combat New Delhi Metallo-β-lactamase-1 (NDM-1) enzyme-mediated antibiotic resistance. This method significantly improved classification accuracy compared to traditional techniques.

Keywords:
NDM-1 inhibitorsdrug discoverymachine learningmultiple instance learning

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Area of Science:

  • Biochemistry and Molecular Biology
  • Computational Biology and Cheminformatics
  • Infectious Diseases and Microbiology

Background:

  • New Delhi Metallo-β-lactamase-1 (NDM-1) is a key enzyme conferring bacterial resistance to a broad spectrum of antibiotics.
  • Managing and predicting NDM-1 activity is crucial for developing new antimicrobial strategies.
  • Existing methods for analyzing antibiotic resistance mechanisms require enhancement for accuracy and scope.

Purpose of the Study:

  • To establish a curated database of NDM-1 bioactivities.
  • To develop and validate a novel computational framework for classifying NDM-1 activity.
  • To identify potent compounds targeting NDM-1 through database scanning.

Main Methods:

  • Creation of a unified NDM-1 bioactivities database with standardized rules.
  • Application of Multiple Instance Learning (MIL) with molecular substructure embeddings.
  • Development of an ensemble ranking and classification framework using k-fold Cross-Validation and hyper-parameter optimization.
  • Investigation of compact molecular representations (atomic and bi-atomic substructures).
  • Screening of the Drugbank database for highly active compounds.

Main Results:

  • The developed MIL paradigm demonstrated a significant improvement of up to 45.7% in Balanced Accuracy over classical Machine Learning approaches.
  • The framework showed promising generalization ability in classifying NDM-1 activity.
  • The study identified and ranked the top 15 most active compounds against NDM-1 from the Drugbank.

Conclusions:

  • The novel MIL-based computational framework offers a powerful and accurate method for analyzing NDM-1 bioactivities.
  • The curated database and established rules provide a valuable resource for NDM-1 research.
  • The identified potent compounds represent promising candidates for future drug development against NDM-1-mediated antibiotic resistance.