Retrieving novel C5aR antagonists using a hybrid ligand-based virtual screening protocol based on SVM classification...

Area of Science:

• Medicinal Chemistry
• Computational Chemistry
• Immunology

Background:

• C5a receptor (C5aR) antagonists are crucial for managing inflammatory and autoimmune diseases.
• Traditional high-throughput screening for C5aR antagonists is costly and inefficient.
• Computer-aided virtual screening (VS) offers a cost-effective and rapid alternative for drug discovery.

Purpose of the Study:

• To develop and evaluate a hybrid ligand-based VS protocol for identifying novel C5aR antagonists.
• To improve the efficiency and success rate of lead discovery for C5aR antagonists.
• To apply the validated VS protocol to large chemical libraries for novel compound retrieval.

Main Methods:

• Proposed a hybrid VS protocol integrating support vector machine (SVM) classification and pharmacophore models.
• Evaluated the hybrid VS protocol's performance against a large independent test set (T-CHEM).
• Compared the hybrid approach with individual SVM-based VS, pharmacophore-based VS, and molecular docking-based VS.

Main Results:

• The hybrid VS approach significantly enhanced hit rates and enrichment factors.
• It outperformed individual SVM and pharmacophore models, as well as homology modeling-based molecular docking.
• Screening of large chemical libraries (PubChem, Specs, Enamine) yielded 20 promising compounds.

Conclusions:

• The hybrid VS protocol is a highly effective strategy for discovering novel C5aR antagonists.
• This method offers a significant improvement over existing VS techniques.
• The identified compounds warrant further in vitro and in vivo investigation for therapeutic potential.