Flexible ligand docking using differential evolution

Molecular docking of biomolecules is becoming an increasingly important part in the process of developing new drugs, as well as searching compound databases for promising drug candidates. The docking of ligands to proteins can be formulated as an optimization problem where the task is to find the most favorable energetic conformation among the large space of possible protein-ligand complexes. Stochastic search methods, such as evolutionary algorithms (EAs), can be used to sample large search spaces effectively and is one of the preferred methods for flexible ligand docking. The differential evolution algorithm (DE) is applied to the docking problem using the AutoDock program. The introduced DockDE algorithm is compared with the Lamarckian GA (LGA) provided with AutoDock, and the DockEA previously found to outperform the LGA. The comparison is performed on a suite of six commonly used docking problems. In conclusion, the introduced DockDE outperformed the other algorithms on all problems. Further, the DockDE showed remarkable performance in terms of convergence speed and robustness regarding the found solution.