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Artificial intelligence driven innovation in tuberculosis drug discovery.

Nibedita Rath1, V K Arora2

  • 1Open Source Pharma Foundation, Manyata Tech Park, MFAR Green Heart Building, Level 7 Hebbal, Outer Ring Road, Bangalore, 560045, India.

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Summary

Artificial intelligence (AI) is revolutionizing tuberculosis (TB) drug discovery by overcoming traditional pipeline hurdles. AI methods accelerate the identification and design of novel anti-TB drugs, though challenges in validation and generalizability remain.

Keywords:
Artificial intelligenceDrug repurposingGenerative modelsMachine learningResistance genomicsTuberculosis

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Area of Science:

  • Computational Biology
  • Medicinal Chemistry
  • Drug Discovery

Background:

  • Traditional tuberculosis (TB) drug discovery faces significant challenges including long timelines, high costs, and complex Mycobacterium tuberculosis (Mtb) biology.
  • Existing methods struggle with efficiency in identifying novel drug candidates and optimizing therapeutic strategies.

Purpose of the Study:

  • To review the current applications of artificial intelligence (AI) in tuberculosis drug discovery.
  • To compare AI-driven approaches with traditional methods, highlighting advances and limitations.
  • To identify future opportunities for computational innovation in TB therapeutics.

Main Methods:

  • Systematic analysis of large biological and chemical datasets using machine learning (ML) and deep learning (DL).
  • Application of AI across multiple drug development stages: target identification, virtual screening, de novo drug design, preclinical optimization, and clinical trial design.
  • Integration of resistance genomics for developing durable therapeutic strategies.

Main Results:

  • AI accelerates the identification of novel drug targets and candidates active against drug-sensitive and drug-resistant Mtb.
  • AI enhances virtual screening, predictive modeling of anti-TB activity, and de novo drug design.
  • AI aids in prioritizing compounds with favorable pharmacokinetic and safety profiles.

Conclusions:

  • AI significantly improves efficiency and effectiveness in TB drug discovery compared to traditional methods.
  • Challenges such as limited model generalizability, lack of mechanistic insight, and data quality require further attention.
  • Continued integration of AI and computational approaches holds promise for advancing TB therapeutics and combating drug resistance.