Division of Laboratory and Genomic Medicine, Department of Pathology and Immunology, Washington University School of Medicine, St Louis, MO, USA.
Bioinformatics. 2011 Aug 15;27(16):2271-8. doi: 10.1093/bioinformatics/btr369. Epub 2011 Jun 17.
In high-throughput screens (HTS) of small molecules for activity in an in vitro assay, it is common to search for active scaffolds, with at least one example successfully confirmed as an active. The number of active scaffolds better reflects the success of the screen than the number of active molecules. Many existing algorithms for deciding which hits should be sent for confirmatory testing neglect this concern.
We derived a new extension of a recently proposed economic framework, diversity-oriented prioritization (DOP), that aims-by changing which hits are sent for confirmatory testing-to maximize the number of scaffolds with at least one confirmed active. In both retrospective and prospective experiments, DOP accurately predicted the number of scaffold discoveries in a batch of confirmatory experiments, improved the rate of scaffold discovery by 8-17%, and was surprisingly robust to the size of the confirmatory test batches. As an extension of our previously reported economic framework, DOP can be used to decide the optimal number of hits to send for confirmatory testing by iteratively computing the cost of discovering an additional scaffold, the marginal cost of discovery.
Supplementary data are available at Bioinformatics online.
在体外测定中小分子活性的高通量筛选（HTS）中，通常会寻找有活性的支架，其中至少有一个成功确认为有活性。有活性的支架数量比有活性的分子数量更能反映筛选的成功。许多现有的用于决定哪些命中物应进行确证性测试的算法忽略了这一关注点。
我们推导出了最近提出的经济框架多样性导向优先级化（DOP）的一个新扩展，该扩展旨在通过改变发送进行确证性测试的命中物来最大化至少有一个确认活性的支架数量。在回顾性和前瞻性实验中，DOP 准确地预测了一批确证性实验中的支架发现数量，将支架发现率提高了 8-17%，并且对确证性测试批次的大小具有惊人的鲁棒性。作为我们之前报道的经济框架的扩展，DOP 可以通过迭代计算发现额外支架的成本，即发现的边际成本，来决定发送进行确证性测试的最佳命中物数量。
补充数据可在“Bioinformatics”在线获取。
