Artificial intelligence driven innovation in tuberculosis drug discovery.

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.