Schneider Gisbert, Hartenfeller Markus, Reutlinger Michael, Tanrikulu Yusuf, Proschak Ewgenij, Schneider Petra
Johann Wolfgang Goethe-University, Institute of Organic Chemistry and Chemical Biology, LiFF, Siesmayerstr. 70, D-60323 Frankfurt am Main., Germany.
Trends Biotechnol. 2009 Jan;27(1):18-26. doi: 10.1016/j.tibtech.2008.09.005. Epub 2008 Nov 10.
De novo drug design has emerged as a valuable concept for the rapid identification of lead structure candidates. In particular, fragment-based molecular assembly methods have been successfully employed for the automated design of screening compounds. Here, we review the current status of these approaches, with an emphasis on adaptive techniques that can be used to artificially evolve novel bioactive molecules. Evolutionary algorithms (EAs) and particle swarm optimization (PSO) are presented as preferred techniques for iterative virtual synthesis and testing. By the inclusion of straightforward synthesis rules, druglike compounds can be obtained. Evolving compound libraries are particularly suited for hit and lead finding in situations where resources are limited and the complete testing of a large screening compound collection is prohibitive.
从头药物设计已成为快速识别潜在结构候选物的一个有价值的概念。特别是，基于片段的分子组装方法已成功用于筛选化合物的自动化设计。在此，我们综述这些方法的当前状况，重点关注可用于人工进化新型生物活性分子的自适应技术。进化算法（EAs）和粒子群优化（PSO）被作为迭代虚拟合成和测试的首选技术。通过纳入直接的合成规则，可获得类药物化合物。在资源有限且对大型筛选化合物集合进行全面测试成本过高的情况下，不断进化的化合物库特别适合于发现活性化合物和潜在药物。
