Methods for computer-aided chemical biology. Part 3: analysis of structure-selectivity relationships through single- or dual-step selectivity searching and Bayesian classification.
The identification of small molecules that are selective for individual targets within target families is an important task in chemical biology. We aim at the development of computational approaches for the study of structure-selectivity relationships and prediction of target-selective ligands. In previous studies, we have introduced the concept of selectivity searching. Here we study compound selectivity on the basis of 18 selectivity sets that are designed to contain target-selective molecules and compounds that are comparably active against related targets. These sets consist of a total of 432 compounds and focus on eight targets belonging to four target families. This compound source has enabled us to evaluate different computational approaches to search for target-selective compounds in large databases. These investigations have revealed a preferred search strategy to enrich database selection sets with target-selective compounds. The selectivity sets reported here are made publicly available to support the development of other computational tools for applications in chemical biology and medicinal chemistry.
Stumpfe
Dagmar
D
Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität Bonn, Dahlmannstr. 2, D-53113 Bonn, Germany.
Geppert
Hanna
H
Bajorath
Jürgen
J
D001499:Bayes Theorem; D017076:Computer-Aided Design; D003205:Computing Methodologies; D015394:Molecular Structure; D009930:Organic Chemicals / Q000737:chemistry*; D013329:Structure-Activity Relationship
D009930:Organic Chemicals