Muresan Sorel, Sadowski Jens
AstraZeneca R and D Mölndal, Pepparedsleden 1, 43183 Mölndal, Sweden.
J Chem Inf Model. 2005 Jul-Aug;45(4):888-93. doi: 10.1021/ci049702o.
Binary classification models able to discriminate between data sets of compounds are useful tools in a range of applications from compound acquisition to library design. In this paper we investigate the ability of artificial neural networks to discriminate between compound collections from various sources aiming at developing an "in-house likeness" scoring scheme (i.e. in-house vs external compounds) for compound acquisition. Our analysis shows atom-type based Ghose-Crippen fingerprints in combination with artificial neural networks to be an efficient way to construct such filters. A simple measure of the chemical overlap between different compound collections can be derived using the output scores from the neural net models.
能够区分化合物数据集的二元分类模型是从化合物获取到库设计等一系列应用中的有用工具。在本文中，我们研究了人工神经网络区分来自各种来源的化合物集合的能力，旨在开发一种用于化合物获取的“内部相似性”评分方案（即内部化合物与外部化合物）。我们的分析表明，基于原子类型的戈什-克里彭指纹与人工神经网络相结合是构建此类过滤器的有效方法。可以使用神经网络模型的输出分数得出不同化合物集合之间化学重叠的简单度量。
