title: Compound Data Mining for Drug Discovery.
abstract: In recent years, there has been unprecedented growth in compound activity data in the public domain. These compound data provide an indispensable resource for drug discovery in academic environments as well as in the pharmaceutical industry. To handle large volumes of heterogeneous and complex compound data and extract discovery-relevant knowledge from these data, advanced computational mining approaches are required. Herein, major public compound data repositories are introduced, data confidence criteria reviewed, and selected data mining approaches discussed.
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. bajorath@bit.uni-bonn.de.
authors.forename: Jürgen
authors.initials: J
authors.lastname: Bajorath
mesh_terms: D057225:Data Mining / Q000379:methods*; D016208:Databases, Factual; D015195:Drug Design; D055808:Drug Discovery / Q000379:methods*; D013329:Structure-Activity Relationship
keywords: Activity cliffs; Activity profiles; Compound activity data; Confidence criteria; Matched molecular pairs; Public databases; Structure–activity relationships