Hull R D, Singh S B, Nachbar R B, Sheridan R P, Kearsley S K, Fluder E M
Department of Molecular Systems, RY50S-100, Merck Research Laboratories, P.O. Box 2000, Rahway, New Jersey 07065, USA.
J Med Chem. 2001 Apr 12;44(8):1177-84. doi: 10.1021/jm000393c.
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.
描述了一种从化学子结构描述符计算化学相似性的新方法。这种名为LaSSI的新方法使用化学描述符 - 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。在降维空间中按与探针分子的相似性对分子进行排名比在原始描述符空间中的类似排名具有几个优点：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一种合理的方法来改变搜索的“模糊度”，并且化学空间维度的降低提高了搜索速度。LaSSI还允许计算两个描述符之间以及一个描述符与一个分子之间的相似性。
