High-performance drug discovery: computational screening by combining docking and molecular dynamics simulations.

Area of Science:

• Computational chemistry
• Drug discovery
• Molecular modeling

Background:

• Virtual compound screening using molecular docking is a common but unreliable method for identifying new drug lead compounds.
• The limitations of molecular docking necessitate improved computational strategies for enhanced reliability in drug design.

Purpose of the Study:

• To enhance the enrichment performance of molecular docking in virtual screening.
• To improve the reliability and effectiveness of lead compound discovery through computational methods.

Main Methods:

• Utilized massive molecular dynamics (MD) simulations on protein-ligand conformations generated by molecular docking.
• Employed the molecular mechanics/Poisson-Boltzmann and surface area (MM/PBSA) method to calculate binding free energies.
• Performed approximately 6,000 MD simulations for the top 1,000 docked compounds, considering multiple docking poses.

Main Results:

• The study demonstrated a 1.6-4.0 fold improvement in enrichment performance for the top 100 compounds compared to standard molecular docking.
• Massive molecular dynamics simulations significantly boosted the accuracy of virtual screening for lead compound identification.

Conclusions:

• The integration of molecular dynamics simulations with molecular docking presents an effective and practical approach for virtual screening in drug discovery.
• Further optimization of computational protocols is necessary to broaden the applicability of this enhanced screening method across diverse target proteins.