Abstract
Protein-ligand docking is an essential part of computer-aided drug design, and it identifies the binding patterns of proteins and ligands by computer simulation. Though Lamarckian genetic algorithm (LGA) has demonstrated excellent performance in terms of protein-ligand docking problems, it can not memorize the history information that it has accessed, rendering it effort-consuming to discover some promising solutions. This article illustrates a novel optimization algorithm (HIGA), which is based on LGA for solving the protein-ligand docking problems with an aim to overcome the drawback mentioned above. A running history information guided model, which includes CE crossover, ED mutation, and BSP tree, is applied in the method. The novel algorithm is more efficient to find the lowest energy of protein-ligand docking. We evaluate the performance of HIGA in comparison with GA, LGA, EDGA, CEPGA, SODOCK, and ABC, the results of which indicate that HIGA outperforms other search algorithms.
Highlights
Drug molecular design, as a new drug research method and means, has achieved a lot of theoretical and practical research findings [1,2,3,4]
We report an Lamarckian genetic algorithm (LGA)-based novel genetic algorithm that enhances the performance of protein-ligand docking by utilizing the running history information in this article
PDBbind 2016 to make up Dataset 1, and we choose sixteen complexes that have a different number of rotatable bonds in ligands from the hundred complexes to constitute Dataset 2
Summary
As a new drug research method and means, has achieved a lot of theoretical and practical research findings [1,2,3,4]. Protein-ligand docking is a typical method for structure-based drug discovery and design, the aim of which is to find the best ligand conformation of a ligand against a protein receptor target with the lowest energy [5,6,7,8,9]. The progress of X-ray diffraction technology of biological macromolecules provides us with more important structures of proteins and ligands. These structures can be used as targets for bioactive substances to control diseases in animals and plants, and they allow for people to understand the biological mechanisms of active substances [10,11,12]. The ligands are placed at the active site of the receptors, the position and orientation of the ligands are adjusted by some binding and complementary principles, and the optimal binding modes are obtained
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