Extended-Connectivity Fingerprints

被引:4746
作者
Rogers, David [1 ]
Hahn, Mathew [1 ]
机构
[1] Accelrys Inc, San Diego, CA 92121 USA
关键词
THROUGHPUT SCREENING DATA; SIGNATURE MOLECULAR DESCRIPTOR; NAIVE BAYES CLASSIFIER; DATA FUSION; NEAREST-NEIGHBOR; MDL KEYS; SIMILARITY; PREDICTION; DOCKING; GENERATION;
D O I
10.1021/ci100050t
中图分类号
R914 [药物化学];
学科分类号
100701 ;
摘要
Extended-connectivity fingerprints (ECEPs) are a novel class of topological fingerprints for molecular characterization. Historically, topological fingerprints were developed for substructure and similarity searching. ECEPs were developed specifically for structure activity modeling. ECEPs are circular fingerprints with a number of useful qualities: they can be very rapidly calculated; they are not predefined and can represent an essentially infinite number of different molecular features (including stereochemical information); their features represent the presence of particular substructures, allowing easier interpretation of analysis results; and the ECFP algorithm can he tailored to generate different types of circular fingerprints, optimized for different uses. While the use of ECEPs has been widely adopted and validated, a description of their implementation has not previously been presented in the literature.
引用
收藏
页码:742 / 754
页数:13
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