SAveRUNNER

SAveRUNNER identifies candidate drug–disease associations for drug repurposing by computing network-based similarity between drug targets and disease-associated proteins within the human interactome.

Key Features:

• R implementation: Implemented in R as the computational environment.
• Network-based approach: Operates on the human interactome using protein–protein interactions to relate drugs and diseases.
• Novel similarity measure: Computes a novel network-based similarity metric to quantify relationships between drugs and diseases.
• Network neighborhood analysis: Assesses proximity of drugs and diseases within the same network neighborhoods.
• Prioritization of associations: Prioritizes drug–disease associations that are likely to be therapeutically relevant based on network proximity.
• Validation: Demonstrated ability to recover known drug indications, supporting predictive accuracy.

Scientific Applications:

• Drug repurposing: Identifying novel indications for existing marketed drugs.
• SARS-CoV-2 / COVID-19: Predicting off-label drugs for repurposing against SARS-CoV-2 (COVID-19).
• Emerging infectious diseases and urgent conditions: Supporting rapid identification of candidate therapeutics for emerging infectious diseases and other conditions requiring new treatments.

Methodology:

Leverages the human interactome (protein–protein interactions) and computes a novel network-based similarity measure that assesses proximity between drug targets and disease-associated proteins within network neighborhoods; implemented in R.