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SCISSORS: Practical Considerations
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AI
DOI：10.1021/ci400264f.png)
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摘要
En 中文
Molecular similarity has been effectively applied to many problems in cheminformatics and computational drug discovery, but modern methods can be prohibitively expensive for large-scale applications. The SCISSORS method rapidly approximates measures of pairwise molecular similarity such as ROCS and LINGO Tanimotos, acting as a filter to quickly reduce the size of a problem. We report an in-depth analysis of SCISSORS performance, including a mapping of the SCISSORS error distribution, benchmarking, and investigation of several algorithmic modifications. We show that SCISSORS can accurately predict multiconformer similarity and suggest a method for estimating optimal SCISSORS parameters in a data set-specific manner. These results are a useful resource for researchers seeking to incorporate SCISSORS into molecular similarity applications.
Keyword:
GAUSSIAN DESCRIPTION
SHAPE
ALGORITHM
DOCKING
LINGO
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5.3
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9.1K
被引数:
4.0W
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