title: SCISSORS: practical considerations.
abstract: 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.
authors.affiliation: Department of Structural Biology, Stanford University , Stanford, California 94305, United States.
authors.forename: Steven M
authors.initials: SM
authors.lastname: Kearnes
authors.forename: Imran S
authors.initials: IS
authors.lastname: Haque
authors.forename: Vijay S
authors.initials: VS
authors.lastname: Pande
mesh_terms: D000465:Algorithms; D019295:Computational Biology; D062126:Databases, Chemical; D055808:Drug Discovery; D004353:Drug Evaluation, Preclinical; D008956:Models, Chemical; D015394:Molecular Structure; D014584:User-Computer Interface