Stahura Florence L, Bajorath Jürgen
Albany Molecular Research, Inc. (AMRI), AMRI Bothell Research Center (AMRI-BRC), 18804 North Creek Pkwy, Bothell, WA 98011, USA.
Comb Chem High Throughput Screen. 2004 Jun;7(4):259-69. doi: 10.2174/1386207043328706.
In this review, we discuss a number of computational methods that have been developed or adapted for molecule classification and virtual screening (VS) of compound databases. In particular, we focus on approaches that are complementary to high-throughput screening (HTS). The discussion is limited to VS methods that operate at the small molecular level, which is often called ligand-based VS (LBVS), and does not take into account docking algorithms or other structure-based screening tools. We describe areas that greatly benefit from combining virtual and biological screening and discuss computational methods that are most suitable to contribute to the integration of screening technologies. Relevant approaches range from established methods such as clustering or similarity searching to techniques that have only recently been introduced for LBVS applications such as statistical methods or support vector machines. Finally, we discuss a number of representative applications at the interface between VS and HTS.
在本综述中,我们讨论了一些已开发或改编用于分子分类和化合物数据库虚拟筛选(VS)的计算方法。特别是,我们重点关注与高通量筛选(HTS)互补的方法。讨论仅限于在小分子水平上运行的VS方法,通常称为基于配体的VS(LBVS),不考虑对接算法或其他基于结构的筛选工具。我们描述了从虚拟筛选与生物筛选相结合中获益匪浅的领域,并讨论了最适合促进筛选技术整合的计算方法。相关方法涵盖从聚类或相似性搜索等成熟方法到最近才引入用于LBVS应用的技术,如统计方法或支持向量机。最后,我们讨论了VS和HTS之间接口处的一些代表性应用。