Blueprint for Drug Repurposing Success: Foundational Concepts and Practical Framework

Area of Science:

• Drug discovery and development
• Computational biology
• Pharmacology

Background:

• Drug repurposing offers a faster, more cost-effective alternative to conventional drug development.
• Repurposed drugs leverage established safety, pharmacokinetic, and clinical data.
• This approach significantly reduces the time and financial investment required for new therapies.

Purpose of the Study:

• To provide a comprehensive analysis of traditional and computational drug repurposing strategies.
• To examine the integration and impact of artificial intelligence (AI) and machine learning (ML) in drug repurposing.
• To compare computational platforms, virtual screening tools, and bioinformatics resources for drug discovery.

Main Methods:

• Experimental methods: binding affinity assays, clinical data mining, phenotype-based screening.
• Computational strategies: structure-based, signature-based, pathway-based, knowledge-based, target-based approaches.
• AI/ML integration: analyzing large datasets, predicting drug-target interactions, advancing repurposing pipelines.

Main Results:

• AI and ML enhance the efficiency and predictive accuracy of drug repurposing.
• Emerging AI models like deep learning and graph neural networks show transformative potential.
• A systematic comparison of computational tools highlights their strengths and limitations.

Conclusions:

• Drug repurposing, augmented by AI/ML, is a powerful innovation driver in drug discovery.
• Effective utilization of computational resources can expedite the identification of novel therapeutic applications.
• This review assists scientists in leveraging existing resources for efficient drug repurposing efforts.