A comparison of heuristic search algorithms for molecular docking

Area of Science:

• Computational chemistry
• Bioinformatics
• Drug discovery

Background:

• Flexible molecular docking is crucial for understanding protein-ligand interactions.
• Heuristic search algorithms are widely used to explore complex conformational spaces.
• Previous studies have not extensively compared various heuristic methods for molecular docking.

Purpose of the Study:

• To implement and compare four heuristic search algorithms (genetic algorithm, evolutionary programming, simulated annealing, and tabu search) against random search for flexible molecular docking.
• To evaluate the performance of these algorithms using a fast molecular recognition potential function and diverse protein-ligand systems.
• To assess the suitability of tabu search, a novel approach in this domain, for molecular docking.

Main Methods:

• Implementation of genetic algorithm, evolutionary programming, simulated annealing, tabu search, and random search.
• Utilized a recently developed fast molecular recognition potential function for energy calculations.
• Tested algorithms on a diverse set of five protein-ligand systems to ensure broad applicability.

Main Results:

• Genetic algorithm demonstrated the best overall performance based on the median energy of located solutions.
• Tabu search exhibited superior performance in identifying ligand conformations close to the crystallographic structure.
• Statistical analysis confirmed significant performance differences among the evaluated algorithms.

Conclusions:

• Genetic algorithm is a strong performer for general molecular docking tasks.
• Tabu search shows promise for accurately reproducing known binding poses.
• Hybrid search algorithms combining strengths of different methods may offer enhanced performance for flexible molecular docking.