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摘要
En 中文
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.
Keyword:
THROUGHPUT SCREENING DATA
SIGNATURE MOLECULAR DESCRIPTOR
NAIVE BAYES CLASSIFIER
DATA FUSION
NEAREST-NEIGHBOR
MDL KEYS
SIMILARITY
PREDICTION
DOCKING
GENERATION
期刊
IF:
5.3
论文数:
9.2K
被引数:
4.0W
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