Bajorath Jürgen
Department of Computer-Aided Drug Discovery, Albany Molecular Research, Inc., Bothell Research Center, 18804 North Creek Parkway, Bothell, Washington 98011, USA.
Nat Rev Drug Discov. 2002 Nov;1(11):882-94. doi: 10.1038/nrd941.
High-throughput and virtual screening are important components of modern drug discovery research. Typically, these screening technologies are considered distinct approaches, as one is experimental and the other is theoretical in nature. However, given their similar tasks and goals, these approaches are much more complementary to each other than often thought. Various statistical, informatics and filtering methods have recently been introduced to foster the integration of experimental and in silico screening and maximize their output in drug discovery. Although many of these ideas and efforts have not yet proceeded much beyond the conceptual level, there are several success stories and good indications that early-stage drug discovery will benefit greatly from a more unified and knowledge-based approach to biological screening, despite the many technical advances towards even higher throughput that are made in the screening arena.
高通量筛选和虚拟筛选是现代药物发现研究的重要组成部分。通常，这些筛选技术被视为不同的方法，因为一种是实验性的，另一种本质上是理论性的。然而，鉴于它们相似的任务和目标，这些方法之间的互补性比通常认为的要强得多。最近已经引入了各种统计、信息学和过滤方法，以促进实验性筛选和计算机模拟筛选的整合，并在药物发现中最大化它们的产出。尽管这些想法和努力中的许多尚未超越概念层面，但有几个成功案例和良好迹象表明，尽管筛选领域在实现更高通量方面取得了许多技术进步，但早期药物发现将从更统一、基于知识的生物筛选方法中受益匪浅。
