Willett P
Krebs Institute for Biomolecular Research, Department of Information Studies, University of Sheffield, UK.
Trends Biotechnol. 1995 Dec;13(12):516-21. doi: 10.1016/S0167-7799(00)89015-0.
Genetic algorithms provide a novel tool for the investigation of combinatorial optimization problems. A genetic algorithm takes an initial set of possible starting solutions, and iteratively improves them by means of crossover and mutation operators that are related to those involved in Darwinian evolution. This approach is illustrated by reference to applications in molecular modelling, the docking of flexible ligands into protein active sites and de novo ligand design.
遗传算法为组合优化问题的研究提供了一种新颖的工具。遗传算法采用一组初始的可能起始解,并通过与达尔文进化中涉及的交叉和变异算子相关的方式对其进行迭代改进。通过分子建模、将柔性配体对接至蛋白质活性位点以及从头设计配体等应用实例对该方法进行了说明。