DrugRep: an automatic virtual screening server for drug repurposing.

Area of Science:

• Computational chemistry
• Drug discovery
• Bioinformatics

Background:

• Drug repurposing offers advantages over de novo drug development, including reduced risk, cost, and development time.
• Computational methods are crucial for accelerating the identification of new therapeutic applications for existing drugs.

Purpose of the Study:

• To develop an automated and parameter-free virtual screening server, DrugRep, to facilitate computational drug repurposing.
• To integrate both receptor-based and ligand-based screening approaches within a single platform.

Main Methods:

• DrugRep performs automated molecular 3D structure construction, binding pocket prediction, docking (using AutoDock Vina), and similarity comparisons.
• Receptor-based screening involves automated cavity detection and batch docking.
• Ligand-based screening utilizes seven established similarity tools, including LigMate and FitDock.

Main Results:

• DrugRep provides a fully automated workflow for virtual screening and drug repurposing.
• The server integrates diverse computational methods for comprehensive drug target identification.
• It offers user-friendly graphical interfaces and interactive predictions with high accuracy.

Conclusions:

• DrugRep is a valuable, freely available online tool that enhances the efficiency of drug repurposing.
• The platform is expected to benefit the broader drug discovery community by accelerating the identification of novel drug applications.