A large-scale computational approach to drug repositioning.

Area of Science:

• Computational chemistry
• Pharmacology
• Drug discovery

Background:

• Drug repositioning offers a cost-effective strategy for developing new therapeutics.
• Predicting protein-small molecule interactions is crucial for understanding drug efficacy and identifying new applications.

Purpose of the Study:

• To develop and validate a computational pipeline for predicting protein-small molecule interactions.
• To apply this pipeline to drug repositioning by identifying novel protein targets for existing drugs.

Main Methods:

• Developed a computational pipeline combining forward and inverse docking.
• Collected a dataset of 1,055 approved small molecule drugs and 1,548 drug target binding pockets.
• Performed large-scale docking using ICM software to predict interactions and validate the method.

Main Results:

• Docked conformations for known protein-drug interactions were highly accurate (within 2.0Å).
• Identified 52 instances where a drug docked better to a novel protein target than its known target.
• These novel interactions suggest potential new therapeutic indications for existing drugs.

Conclusions:

• The developed computational pipeline, particularly inverse docking, is effective for predicting protein-small molecule interactions.
• Inverse docking holds significant promise for identifying novel therapeutic uses of existing drugs.
• This approach can accelerate drug discovery and development by uncovering new indications for approved medications.