Unlocking potent anti-tuberculosis natural products through structure-activity relationship analysis

Area of Science:

• Medicinal Chemistry
• Natural Products Chemistry
• Computational Chemistry

Background:

• Tuberculosis (TB) poses a significant global health challenge due to drug resistance and prolonged treatment.
• There is an urgent need for novel anti-TB drugs with new mechanisms of action.
• Natural products are a rich source of diverse bioactive compounds for drug discovery.

Purpose of the Study:

• To review potent anti-TB natural products (MIC < 5 µg/mL).
• To examine the structure-activity relationships (SAR) of these compounds.
• To assess the correlation between anti-TB potency and cytotoxicity.

Main Methods:

• Literature review of natural products with anti-TB activity.
• Analysis of Structure-Activity Relationships (SAR) using Random Forest machine learning.
• Molecular docking studies with AutoDock Vina to predict target interactions.
• XGBoost machine learning model to enhance predictive accuracy.

Main Results:

• Identified potent natural products with significant anti-TB activity.
• Elucidated key structural features contributing to anti-TB efficacy through SAR analysis.
• Evaluated the relationship between compound potency and host cell cytotoxicity.

Conclusions:

• Natural products represent a valuable resource for novel anti-TB drug discovery.
• Understanding SAR and cytotoxicity is crucial for identifying promising drug scaffolds.
• Computational methods aid in predicting and optimizing anti-TB compound efficacy.