Enhancing the Reliability of Integrated Consensus Strategies to Boost Docking-Based Screening Campaigns Using...

Area of Science:

• Computational chemistry and cheminformatics
• Drug discovery and development
• Bioinformatics and computational biology

Background:

• Molecular docking is crucial for drug discovery, but its performance varies with protein targets and software.
• Consensus docking, integrating multiple docking programs, improves high-throughput screening (HTS) reliability.
• Existing consensus methods often focus on either pose or ranking, necessitating a combined approach.

Purpose of the Study:

• To develop and evaluate an integrated pose and ranking consensus approach for molecular docking.
• To enhance the performance of high-throughput docking (HTD) using publicly available software.
• To assess the effectiveness of the combined approach in identifying potential drug candidates.

Main Methods:

• Utilized five publicly available docking programs: rDock, DOCK 6, Auto Dock 4, PLANTS, and Vina.
• Developed an integrated approach combining pose consensus and exponential consensus ranking (ECR).
• Benchmarked the approach using 50 diverse protein targets and property-matched ligand/decoy libraries.

Main Results:

• The enhanced pose/ranking consensus approach significantly outperformed individual docking programs and standard ECR.
• Evaluated in HTD campaigns with ~1.1 million molecules across six targets, achieving an average ECR improvement of ~40%.
• Demonstrated superior performance in identifying active compounds compared to single-program docking.

Conclusions:

• The integrated pose/ranking consensus methodology offers a robust and reliable enhancement for HTD.
• This approach can be confidently applied in prospective HTD campaigns utilizing freely available docking programs.
• The study validates consensus docking as a powerful strategy to improve virtual screening efficiency in drug discovery.