Optimizing Dvl PDZ domain inhibitor by exploring chemical space

Area of Science:

• Medicinal Chemistry
• Computational Drug Discovery
• Structural Biology

Background:

• High-throughput screening simplifies initial hit identification but challenges remain in hit-to-lead and lead optimization.
• Traditional synthesis of analogues for structure-activity relationship (SAR) analysis is labor-intensive.
• In silico methods offer an efficient alternative for exploring SAR with large compound libraries.

Purpose of the Study:

• To discover novel Dishevelled PDZ domain inhibitors using computational approaches.
• To develop and apply a pharmacophore model for virtual screening.
• To identify potent inhibitors with enhanced binding affinity.

Main Methods:

• Developed a pharmacophore model based on a known inhibitor (NSC668036).
• Screened the ChemDiv database using a combined similarity search and docking algorithm.
• Selected and validated 15 potent inhibitors using NMR spectroscopy.

Main Results:

• Identified 15 new Dishevelled PDZ domain inhibitors.
• All selected compounds demonstrated tighter binding to the PDZ domain compared to NSC668036.
• Computational screening successfully guided the discovery of potent inhibitors.

Conclusions:

• In silico screening is an effective strategy for identifying potent drug leads.
• The discovered inhibitors represent promising candidates for further drug development targeting the Dishevelled PDZ domain.
• This approach accelerates the hit-to-lead optimization process in drug discovery.