Latent Semantic Structure Indexing

Latent Semantic Structure Indexing (LaSSI) is a technique for calculating chemical similarity derived from Latent semantic analysis (LSA).

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LaSSI was developed at Merck & Co. and patented in 2007 [1] by Richard Hull, Eugene Fluder, Suresh Singh, Robert Sheridan, Robert Nachbar and Simon Kearsley.

Overview

LaSSI 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

• Hull, R.D., Fluder, E.M., Singh, S.B., Nachbar, R.B., Sheridan, R.P. and Kearsley, S.K. (2001) "Latent semantic structure indexing (LaSSI) for defining chemical similarity." J Med Chem, 2001 Apr 12;44(8):1177-84.

• Hull, R.D., Singh, S.B., Nachbar, R.B., Sheridan, R.P., Kearsley, S.K. and Fluder, E.M. (2001) "Chemical similarity searches using latent semantic structure indexing (LaSSI) and comparison to TOPOSIM." J Med Chem, 2001 Apr 12;44(8):1185-91.

• Singh, S.B., Sheridan, R.P., Fluder, E.M. and Hull, R.D. (2001) "Mining the chemical quarry with joint chemical probes: an application of latent semantic structure indexing (LaSSI) and TOPOSIM (Dice) to chemical database mining." J Med Chem, 2001 May 10;44(10):1564-75.