dockECR: Open consensus docking and ranking protocol for virtual screening of small molecules

Area of Science:

• Computational chemistry and drug discovery
• Bioinformatics and cheminformatics
• Structural biology

Background:

• Accelerating the discovery of treatments for emerging diseases requires efficient computational methods.
• Consensus molecular docking enhances virtual screening by improving the identification of active compounds.
• Open computational pipelines facilitate rapid development of novel therapeutic solutions.

Purpose of the Study:

• To introduce dockECR, an open consensus docking and ranking protocol.
• To implement the exponential consensus ranking method for prioritizing molecular candidates.
• To integrate a scoring strategy based on average RMSD for pose prediction.

Main Methods:

• Utilized four open-source molecular docking programs: AutoDock Vina, Smina, LeDock, and rDock.
• Developed a scoring strategy using average RMSD from best poses of individual programs.
• Benchmarked the protocol on 15 protein targets and applied it to SARS-CoV-2 main protease.

Main Results:

• The dockECR protocol successfully ranked molecular candidates for SARS-CoV-2 main protease.
• Eight candidate molecules were prioritized based on dockECR ranking and interactions with key protease residues.
• The protocol demonstrated effectiveness in virtual screening campaigns for protein targets.

Conclusions:

• dockECR provides an open and effective approach to accelerate drug discovery through consensus molecular docking.
• The protocol enhances the prioritization of potential drug candidates by combining multiple docking programs and pose information.
• dockECR is a valuable tool for virtual screening campaigns targeting proteins, applicable to emerging diseases.