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Mycobacterium tuberculosis Cell Wall Permeability Model Generation Using Chemoinformatics and Machine Learning

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Drug-resistant tuberculosis (TB) demands new drugs. Machine learning models identified key molecular features for Mycobacterium tuberculosis (M.tb) cell permeability, aiding the discovery of novel TB treatments.

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Area of Science:

  • * Medicinal Chemistry and Drug Discovery
  • * Computational Biology and Cheminformatics
  • * Infectious Diseases and Microbiology

Background:

  • * Rising drug resistance in *Mycobacterium tuberculosis* (*M.tb*) necessitates novel antitubercular agents.
  • * *M.tb*'s complex cell wall and transport systems create permeability barriers, limiting drug efficacy.
  • * Small molecules effective in vitro often fail in vivo due to poor cell penetration.

Purpose of the Study:

  • * To develop machine learning models for predicting *M.tb* cell permeability.
  • * To identify molecular descriptors crucial for drug permeability and activity against *M.tb*.
  • * To discover potential new antimycobacterial drugs through computational drug repurposing.

Main Methods:

  • * Developed and validated machine learning models (XGBoost, random forest, SVM, naive Bayes) using enzyme-based (IC50) and cell-based (MIC) data.
  • * Utilized molecular descriptors including weight, atom type, electrotopological state, and hydrogen bonding.
  • * Employed computational drug repurposing, screening DrugBank, using the PASS server, and docking to *M.tb* targets.

Main Results:

  • * The XGBoost model demonstrated superior performance in classifying *M.tb* permeable and impermeable compounds.
  • * Key determinants of permeability and activity include molecular weight, atom type, and topological descriptors.
  • * Computational screening identified promising drug candidates for further investigation against *M.tb*.

Conclusions:

  • * Machine learning effectively predicts *M.tb* cell permeability, addressing a critical challenge in drug development.
  • * Understanding key molecular features can guide the design of more effective antitubercular drugs.
  • * Computational drug repurposing offers a viable strategy for identifying novel antimycobacterial agents.