Virtual screening system for finding structurally diverse hits by active learning
Virtual screening system for finding structurally diverse hits by active learning
复制标题
DOI：
10.1021/ci700085q
复制
发表时间：
2008-04-01
影响因子：
5.6
通讯作者：
Shimizu, Ryo
中科院分区：
文献类型：
--
作者：
Fujiwara, Yukiko;Yamashita, Yoshiko;Shimizu, Ryo
设计了基于主动学习的“套袋查询”（QBag）和“描述-抽样套袋查询”（QBagDS）两种虚拟筛选策略。QBag策略通过袋装生成多个构效关系规则，并选择化合物对规则进行改进。为了找到许多结构不同的命中，QBagDS策略通过使用描述符采样进行打包来生成规则。他们还可以利用关于命中的先验知识来提高筛选开始时的效率。我们对多个g蛋白偶联受体进行了模拟实验和聚类分析，结果表明QBag和QBagDS策略优于传统的基于相似性的策略，并且同时使用描述子采样和先验知识可以有效地找到许多命中点。我们将套袋与描述子采样策略应用于新命中发现，10个选定的化合物中有4个具有高抑制作用。
Two virtual screening strategies, "query by bagging" (QBag) and "query by bagging with descriptor-sampling" (QBagDS), based on active learning were devised. The QBag strategy generates multiple structure-activity relationship rules by bagging and selects compounds to improve the rules. To find many structurally diverse hits, the QBagDS strategy generates rules by bagging with descriptor sampling. They can also use prior knowledge about hits to improve the efficiency at the beginning of screening. We performed simulation experiments and clustering analysis for several G-protein coupled receptors and showed that the QBag and QBagDS strategies outperform the conventional similarity-based strategy and that using both descriptor sampling and prior knowledge are effective for finding many hits. We applied the bagging with descriptor sampling strategy to novel hit finding, and 4 of the 10 selected compounds showed high inhibition.