Drug Design using the Novel Directivity Genetic Algorithms and Lyapunov Stability Theorem to Decrease the Number of Binding Sites in Order to Enhance the Docking Performance

The application of the energy minimum to enhance the docking performance of CADD is discussed from three aspects, i.e. geometry, energy, and activity. This study applies the energy minimum theorem to solve the objection. A geometry search is performed and compared with the other four methods for classifying receptors. First, this work attempts to improve the speed of computer simulations of protein folding and to propose an improved genetic algorithm to accelerate the binding site search. Second, the paper focuses on energy theme. Lyapunov's stability theorem is adopted to decrease the number of binding sites, thus enhancing the docking performance in computer simulation examples. Finally, various drug-ligand interaction models are employed to compute docking simulations, while the energy minimum theorem is used to judge the approach approximately global energy minimum area and docking stability. This investigation develops the AMBER force field and Ullman's algorithm to support the computer simulation environments. The significance of the eigenvalue λ is analyzed at each protein folding. Moreover, the performance of this study is increased by 25 percents comparing to various binding sites. Additionally, the protein folding and various bond forces in drug-ligand interaction model are discussed. Comparing with the other four geometry search methods and referring to the two been published paper written by Pegg and Camila, the improved genetic algorithms are specified to undertake the search binding site and docking. As a result, the global minimum search and the arithmetic convergence time of 1.16hr can be obtained. Analytical results indicate that the improved genetic algorithm is better than the traditional random methods in terms of processing the geometry graphics operation.