Latent semantic structure indexing (LaSSI) for defining chemical similarity.
Latent semantic structure indexing (LaSSI) for defining chemical similarity.
复制标题
用于定义化学相似性的潜在语义结构索引（LaSSI）。
DOI：
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发表时间：
2001
影响因子：
7.3
通讯作者：
E. Fluder
中科院分区：
文献类型：
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作者：
R. Hull;S. B. Singh;R. Nachbar;R. Sheridan;S. Kearsley;E. Fluder
描述了一种根据化学子结构描述符计算化学相似性的新方法。这种新方法称为 LaSSI，使用化学描述符分子矩阵的奇异值分解 (SVD) 来创建原始描述符空间的低维表示。与原始描述符空间中的类似排序相比，在降维空间中通过与探针分子的相似性对分子进行排序具有几个优点：匹配潜在结构比匹配离散描述符更稳健，选择奇异值的数量提供了改变搜索“模糊性”的合理方法，并且化学空间维数的减少提高了搜索速度。 LaSSI 还允许计算两个描述符之间以及描述符与分子之间的相似性。
A novel method for computing chemical similarity from chemical substructure descriptors is described. This new method, called LaSSI, uses the singular value decomposition (SVD) of a chemical descriptor-molecule matrix to create a low-dimensional representation of the original descriptor space. Ranking molecules by similarity to a probe molecule in the reduced-dimensional space has several advantages over analogous ranking in the original descriptor space: matching latent structures is more robust than matching discrete descriptors, choosing the number of singular values provides a rational way to vary the "fuzziness" of the search, and the reduction in the dimensionality of the chemical space increases searching speed. LaSSI also allows the calculation of the similarity between two descriptors and between a descriptor and a molecule.