Division of Laboratory and Genomic Medicine, Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, Missouri, USA.
J Chem Inf Model. 2012 Jan 23;52(1):29-37. doi: 10.1021/ci2003285. Epub 2011 Dec 20.
Most methods of deciding which hits from a screen to send for confirmatory testing assume that all confirmed actives are equally valuable and aim only to maximize the number of confirmed hits. In contrast, "utility-aware" methods are informed by models of screeners' preferences and can increase the rate at which the useful information is discovered. Clique-oriented prioritization (COP) extends a recently proposed economic framework and aims--by changing which hits are sent for confirmatory testing--to maximize the number of scaffolds with at least two confirmed active examples. In both retrospective and prospective experiments, COP enables accurate predictions of the number of clique discoveries in a batch of confirmatory experiments and improves the rate of clique discovery by more than 3-fold. In contrast, other similarity-based methods like ontology-based pattern identification (OPI) and local hit-rate analysis (LHR) reduce the rate of scaffold discovery by about half. The utility-aware algorithm used to implement COP is general enough to implement several other important models of screener preferences.
大多数决定从筛选中发送哪些命中进行确证性测试的方法都假设所有确证的活性物质都具有同等价值，且仅旨在最大化确证命中的数量。相比之下，“效用感知”方法则基于筛选器偏好的模型，并能提高有用信息的发现速度。基于团块的优先级排序（COP）扩展了最近提出的经济框架，其目的是通过改变哪些命中被发送进行确证性测试，来最大化至少有两个确证活性例子的支架数量。在回顾性和前瞻性实验中，COP 能够准确预测一批确证性实验中团块发现的数量，并将团块发现的速度提高了 3 倍以上。相比之下，其他基于相似性的方法，如基于本体的模式识别（OPI）和局部命中率分析（LHR），则将支架发现的速度降低了约一半。用于实现 COP 的效用感知算法具有足够的通用性，可以实现其他几种重要的筛选器偏好模型。
