title: Hit Expansion from Screening Data Based upon Conditional Probabilities of Activity Derived from SAR Matrices.
abstract: A new methodology for activity prediction of compounds from SAR matrices is introduced that is based upon conditional probabilities of activity. The approach has low computational complexity, is primarily designed for hit expansion from biological screening data, and accurately predicts both active and inactive compounds. Its performance is comparable to state-of-the-art machine learning methods such as support vector machines or Bayesian classification. Matrix-based activity prediction of virtual compounds further extends the spectrum of computational methods for compound design.
authors.affiliation: Department of Life Science Informatics; Bonn-Aachen International Center for Information Technology, Rheinische Friedrich-Wilhelms-Universität Bonn, Dahlmannstr. 2, 53113 Bonn, Germany tel: +49-228-2699-306; fax: +49-228-2699-341.
authors.forename: Disha
authors.initials: D
authors.lastname: Gupta-Ostermann
authors.affiliation: Department of Life Science Informatics; Bonn-Aachen International Center for Information Technology, Rheinische Friedrich-Wilhelms-Universität Bonn, Dahlmannstr. 2, 53113 Bonn, Germany tel: +49-228-2699-306; fax: +49-228-2699-341.
authors.forename: Jenny
authors.initials: J
authors.lastname: Balfer
authors.affiliation: Department of Life Science Informatics; Bonn-Aachen International Center for Information Technology, Rheinische Friedrich-Wilhelms-Universität Bonn, Dahlmannstr. 2, 53113 Bonn, Germany tel: +49-228-2699-306; fax: +49-228-2699-341. bajorath@bit.uni-bonn.de.
authors.forename: Jürgen
authors.initials: J
authors.lastname: Bajorath
mesh_terms: D003198:Computer Simulation; D062126:Databases, Chemical
keywords: Activity prediction; Conditional probabilities; Hit expansion; Matched molecular pairs and series; SAR matrices; Virtual compounds