title: Conditional probabilities of activity landscape features for individual compounds.
abstract: Activity landscape representations aid in the analysis of structure-activity relationships (SARs) of large compound data sets. Landscapes are characterized by features with different SAR information content such as, for example, regions formed by structurally diverse compounds having similar activity or, alternatively, structurally similar compounds with large activity differences, so-called activity cliffs. Modeling of activity landscapes typically requires pairwise comparisons of molecular similarity and potency relationships of compounds in a data set. Consequently, landscape features are generally resolved at the level of compound pairs. Herein, we introduce a methodology to assign feature probabilities to individual compounds. This makes it possible to organize compounds comprising activity landscapes into well-defined SAR categories. Specifically, the calculation of conditional feature probabilities of active compounds provides a balanced and further refined view of activity landscapes with a focus on individual molecules.
authors.affiliation: LIMES Program Unit Chemical Biology and Medicinal Chemistry, Department of Life Science Informatics, Rheinische Friedrich-Wilhelms-Universität, Dahlmannstrasse 2, D-53113 Bonn, Germany.
authors.forename: Martin
authors.initials: M
authors.lastname: Vogt
authors.forename: Preeti
authors.initials: P
authors.lastname: Iyer
authors.forename: Gerald M
authors.initials: GM
authors.lastname: Maggiora
authors.forename: Jürgen
authors.initials: J
authors.lastname: Bajorath
mesh_terms: D055808:Drug Discovery; D048088:Informatics / Q000379:methods*; D011338:Probability Theory; D013329:Structure-Activity Relationship