title: Fast Modeling of Binding Affinities by Means of Superposing Significant Interaction Rules (SSIR) Method.
abstract: The Superposing Significant Interaction Rules (SSIR) method is described. It is a general combinatorial and symbolic procedure able to rank compounds belonging to combinatorial analogue series. The procedure generates structure-activity relationship (SAR) models and also serves as an inverse SAR tool. The method is fast and can deal with large databases. SSIR operates from statistical significances calculated from the available library of compounds and according to the previously attached molecular labels of interest or non-interest. The required symbolic codification allows dealing with almost any combinatorial data set, even in a confidential manner, if desired. The application example categorizes molecules as binding or non-binding, and consensus ranking SAR models are generated from training and two distinct cross-validation methods: leave-one-out and balanced leave-two-out (BL2O), the latter being suited for the treatment of binary properties.
authors.affiliation: Institut de Qu&#237;mica Computacional i Cat&#224;lisi (IQCC) and Departament de Qu&#237;mica, Universitat de Girona, 17071 Girona, Catalonia, Spain. emili.besalu@udg.edu.
authors.forename: Emili
authors.initials: E
authors.lastname: Besalú
mesh_terms: D000465:Algorithms; D057225:Data Mining / Q000379:methods*; D062313:Databases, Pharmaceutical; D008958:Models, Molecular; D015394:Molecular Structure; D013329:Structure-Activity Relationship
keywords: SAR; SSIR method; analogue series; balanced leave-two-out (BL2O) cross-validation; inverse SAR; ranking