Exploring polypharmacology using a ROCS-based target fishing approach.

Area of Science:

• Computational chemistry
• Pharmacology
• Drug discovery

Background:

• Polypharmacology, the ability of a drug to interact with multiple targets, is a key theme in modern drug discovery.
• Understanding these interactions is crucial for developing effective and safe therapeutics.

Purpose of the Study:

• To develop and validate a ligand-based target fishing (LBTF) protocol for studying polypharmacology.
• To assess the potential of this protocol for drug repurposing, target identification, and adverse effect prediction.

Main Methods:

• Generated a chemogenomic database linking protein targets with representative drugs.
• Computed molecular shape and chemistry overlap using ROCS (Rapid Overlay of Chemical Structures) to generate target profiles.
• Validated the LBTF protocol using the Directory of Useful Decoys (DUD) dataset and analyzed Receiver Operator Characteristic (ROC) curves and Area Under the Curve (AUC) values.

Main Results:

• The LBTF protocol demonstrated significantly better performance than random selection for most targets studied.
• The method successfully identified known off-targets for drugs like rimantadine, propranolol, and domperidone, consistent with experimental findings.
• AUC values indicated the efficacy of the ROCS-based approach in virtual screening.

Conclusions:

• The developed ROCS-based LBTF protocol is a promising tool for polypharmacology research.
• This approach has significant potential applications in drug repurposing, identifying targets for orphan compounds, and predicting adverse drug effects.