DockThor-VS: A Free Platform for Receptor-Ligand Virtual Screening

Area of Science:

• Computational chemistry
• Drug discovery
• Bioinformatics

Background:

• Drug discovery relies on accurate prediction of protein-ligand interactions.
• Virtual screening accelerates the identification of potential drug candidates.
• High-performance computing is crucial for large-scale molecular simulations.

Purpose of the Study:

• To introduce DockThor-VS, a free protein-ligand docking server for drug discovery.
• To provide a user-friendly platform for accurate and efficient virtual screening.
• To leverage high-performance computing for enhanced docking experiments.

Main Methods:

• DockThor docking engine: grid-based, flexible-ligand/rigid-receptor docking.
• Multiple-solution genetic algorithm for pose prediction with MMFF94S force field.
• DockTScore empirical scoring function for affinity prediction and ranking.
• User-friendly DockThor Portal for ligand/protein preparation and grid box customization.

Main Results:

• Automatic clustering and ordering of docking results yield diverse binding modes.
• Platform handles highly flexible ligands, including peptides.
• Integration with SDumont high-performance computing enhances speed and efficiency.
• Provides curated datasets for targets like COVID-19 proteins and FDA-approved drugs.

Conclusions:

• DockThor-VS offers a powerful, user-friendly solution for docking-based virtual screening.
• The platform facilitates efficient and accurate drug discovery projects.
• Enables rapid drug repurposing through curated datasets and advanced algorithms.