title: Large-scale assessment of activity landscape feature probabilities of bioactive compounds.
abstract: Activity landscape representations integrate pairwise compound similarity and potency relationships and provide direct access to characteristic structure-activity relationship features in compound data sets. Because pairwise compound comparisons provide the foundation of activity landscape design, the assessment of specific landscape features such as activity cliffs has generally been confined to the level of compound pairs. A conditional probability-based approach has been applied herein to assign most probable activity landscape features to individual compounds. For example, for a given data set compound, it was determined if it would preferentially engage in the formation of activity cliffs or other landscape features. In a large-scale effort, we have determined conditional activity landscape feature probabilities for more than 160,000 compounds with well-defined activity annotations contained in 427 different target-based data sets. These landscape feature probabilities provide a detailed view of how different activity landscape features are distributed over currently available bioactive compounds.
authors.affiliation: Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität , Dahlmannstr. 2, D-53113 Bonn, Germany.
authors.forename: Shilva
authors.initials: S
authors.lastname: Kayastha
authors.forename: Dilyana
authors.initials: D
authors.lastname: Dimova
authors.forename: Preeti
authors.initials: P
authors.lastname: Iyer
authors.forename: Martin
authors.initials: M
authors.lastname: Vogt
authors.forename: Jürgen
authors.initials: J
authors.lastname: Bajorath
mesh_terms: D055808:Drug Discovery / Q000379:methods*; D048088:Informatics / Q000379:methods*; D011336:Probability; D013329:Structure-Activity Relationship