A Hybrid Virtual Screening Protocol Based on Binding Mode Similarity.

Area of Science:

• Computational chemistry
• Drug discovery
• cheminformatics

Background:

• Structure-based virtual screening (SBVS) is crucial for drug discovery.
• Scoring functions in SBVS often fail to accurately estimate binding affinity.
• Ligand-based (LB) methods can complement SBVS to enhance hit rates.

Purpose of the Study:

• To introduce a hybrid protocol for virtual screening.
• To improve the accuracy of hit identification in drug discovery.
• To leverage binding mode similarity for enhanced screening.

Main Methods:

• Developed a hybrid protocol integrating SBVS and LB approaches.
• Utilized binding mode similarity assessment between docked compounds and reference ligands.
• Employed both experimental and computationally modeled poses as references for 3D similarity calculations.

Main Results:

• The hybrid protocol demonstrates a promising strategy for increasing hit rates in virtual screening.
• Validation studies show successful application of the protocol.
• Binding mode similarity calculations are effective using diverse reference poses.

Conclusions:

• Integrating SBVS and LB methods offers a powerful strategy to overcome limitations in binding affinity prediction.
• The described hybrid protocol provides a robust framework for enhancing virtual screening efficiency.
• The assessment of binding mode similarity is a key factor for successful hit identification in drug discovery.