Differential Evolution for Docking Simulations.

Area of Science:

• Computational chemistry
• Drug discovery
• Bioinformatics

Background:

• Protein-ligand docking is crucial for identifying novel drug candidates.
• Docking programs utilize search algorithms and scoring functions.
• Differential evolution is a bioinspired optimization method applicable to docking.

Purpose of the Study:

• To describe a docking protocol using differential evolution (DE) as the search algorithm.
• To demonstrate the application of the MolDock score in conjunction with DE within Molegro Virtual Docker (MVD).
• To provide a reproducible computational workflow for drug discovery.

Main Methods:

• Implementing differential evolution (DE) within Molegro Virtual Docker (MVD) for docking simulations.
• Utilizing the MolDock score as the scoring function.
• Performing docking against protein targets like cyclin-dependent kinase 2, cannabinoid receptor 1, and transmembrane protease serine 2.

Main Results:

• The developed docking protocol successfully generated ligand poses close to experimental crystallographic coordinates.
• The combination of DE and MolDock score proved effective for virtual screening.
• The methodology is validated against established protein targets.

Conclusions:

• Differential evolution is a viable and effective search algorithm for protein-ligand docking simulations.
• The described MVD protocol offers a robust approach for early-stage drug discovery.
• The study provides a practical computational tool and protocol for researchers.