Using extended-connectivity fingerprints with Laplacian-modified Bayesian analysis in high-throughput screening follow-up
Using extended-connectivity fingerprints with Laplacian-modified Bayesian analysis in high-throughput screening follow-up
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DOI：
10.1177/1087057105281365
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发表时间：
2005-10-01
影响因子：
--
通讯作者：
Hahn, M
中科院分区：
文献类型：
--
作者：
Rogers, D;Brown, RD;Hahn, M
本文介绍了使用扩展连接指纹（ECFPs）和拉普拉斯修正贝叶斯分析相结合的抑制大肠杆菌二氢叶酸还原酶的研究。麦克马斯特大学的麦克马斯特高通量筛选实验室提出了一项竞争，以预测50，000种化合物的单独测试集的命中率。虽然这个问题似乎最好用3D方法来解决，但作者表明，2D方法以较低的计算成本提供了令人惊讶的竞争结果。
This article describes the use of a combination of extended-connectivity fingerprints (ECFPs) and Laplacian-modified Bayesian analysis in a study of the inhibition of Escherichia coli dihydrofolate, reductase. The McMaster High-Throughput Z, Screening Lab at McMaster University proposed a competition to predict the hits in a separate test set of 50,000 compounds. Although the problem seemed best approached with 3D methods, the authors show that 2D methods offer surprisingly competitive results with a low computational cost.