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Latent Semantic Structure IndexingLatent Semantic Structure Indexing (LaSSI) is a technique for calculating chemical similarity derived from Latent semantic analysis (LSA). Additional recommended knowledgeLaSSI was developed at Merck & Co. and patented in 2007 [1] by Richard Hull, Eugene Fluder, Suresh Singh, Robert Sheridan, Robert Nachbar and Simon Kearsley. OverviewLaSSI is similar to LSA in that it involves the construction of an occurrence matrix from a corpus of items and the application of singular value decomposition to that matrix to derive latent features. What differs is that the occurrence matrix represents the frequency of two- and three-dimensional chemical descriptors (rather than natural language terms) found within a chemical database of chemical structures. This process derives latent chemical structure concepts that can be used to calculate chemical similarities and structure-activity relationships for drug discovery. References
Categories: Cheminformatics | Drug discovery |
This article is licensed under the GNU Free Documentation License. It uses material from the Wikipedia article "Latent_Semantic_Structure_Indexing". A list of authors is available in Wikipedia. |