QSAR multi-target in drug discovery: a review

Area of Science:

• Medicinal Chemistry
• Computational Chemistry
• Pharmacology

Background:

• Traditional Quantitative Structure-Activity Relationship (QSAR) models typically focus on a single biological target.
• The complexity of biological systems necessitates approaches that consider multiple targets simultaneously.
• The field of multi-target QSAR (mt-QSAR) has gained significant traction in recent years.

Purpose of the Study:

• To review and summarize key advancements in multi-target QSAR (mt-QSAR) methodologies.
• To underscore the growing importance of mt-QSAR in modern drug discovery.
• To introduce a novel mt-QSAR model for predicting bacterial susceptibility.

Main Methods:

• Comprehensive literature review of significant mt-QSAR studies.
• Development of a new mt-QSAR model for predicting compound activity against multiple bacterial targets.
• Application of the novel model to assess susceptibility in Gram-positive and Gram-negative anaerobic bacteria.

Main Results:

• mt-QSAR enables the prediction of a compound's activity against diverse biological targets using a single model.
• The developed mt-QSAR model demonstrates potential for predicting bacterial susceptibility.
• The technique offers a more holistic approach compared to traditional single-target QSAR.

Conclusions:

• mt-QSAR is an increasingly vital tool in drug discovery, offering efficiency and broader predictive power.
• The presented novel mt-QSAR model shows promise for antimicrobial drug development.
• Future research should continue to explore and refine mt-QSAR applications for complex biological challenges.