| Computer-Aided Drug Design (CADD) increases the pace of designing new drug, saves human and material resources involving in the new drugs creation process. Molecular Docking is regarded as one of the most important steps of CADD, mainly being used to search the small molecule that of good affinity with biological macromolecules from Compound database, discovering new patents on lead compound, and thus improving the accuracy and efficiency of drug design, and reducing the pre-drug design cost. In the Molecular docking study, search algorithm and score function have been the major difficulties and hot points. We have conducted search algorithm research in our dissertation, main works are as follows:1.Exploring the optimization to Molecular Docking's conformational search strategy; constructing a new optimization model-AutoDock-Model-for the Molecular Docking with optimization methods;2.Improving AutoDock3 with Clonal Selection Algorithm based on the AutoDock-Model; designing parameters for Clonal Selection Algorithm with Uniform Design Principle, and implementing the AutoDockCLA;3.Improving AutoDock3 with Immune Genetic Algorithm with AutoDock-Model and achieving the AutoDockIGA;4.In order to verify the optimizing ability of AutoDockIGA and AutoDockCLA, we conduct tests on seven protein-ligands complex from the PDB Protein database and compare the results with ones we have obtained from AutoDock3.Tests show that AutoDockCLA has the best optimizing ability, and AutoDockIGA's has similar docking accuracy with AutoDock3 using Lamarckian genetic algorithm, though the former one performs better than the latter's in term of time-consuming. Another AutoDock3 with simulated annealing algorithm has the worst performance. As for dock issue, both AutoDockIGA and AutoDockCLA are better than traditional GA methods, the two algorithms are universal and significant to other complex optimization problem, e.g. optimization of education resource, real parameter, engineering design, etc. |
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