Genetic algorithm with a crossover elitist preservation mechanism for protein-ligand docking

Area of Science:

• Computational chemistry
• Bioinformatics
• Drug discovery

Background:

• Protein-ligand docking is crucial for computer-aided pharmaceutical development.
• Developing efficient algorithms for accurate docking remains a significant challenge.

Purpose of the Study:

• To propose a novel search algorithm, the genetic algorithm with crossover elitist preservation (CEPGA), for protein-ligand docking.
• To enhance the efficiency and robustness of docking algorithms.

Main Methods:

• The proposed CEPGA algorithm incorporates a crossover elitist preservation (CEP) mechanism.
• CEP ensures the retention of elite individuals across generations to improve crossover effectiveness.
• Performance evaluation involved sixteen molecular docking complexes from the RCSB Protein Data Bank.

Main Results:

• CEPGA demonstrated superior performance compared to traditional Genetic Algorithm (GA), Lamarckian Genetic Algorithm (LGA), and SODOCK.
• The algorithm achieved lower energy states and higher accuracy in molecular docking.
• Results indicate CEPGA's reliability and success in addressing protein-ligand docking challenges.

Conclusions:

• The developed CEPGA is a reliable and successful method for protein-ligand docking.
• The CEP mechanism enhances the efficiency and robustness of genetic algorithms in this field.
• CEPGA offers a promising approach for accelerating computer-aided drug discovery.