Data structures and computational tools for the extraction of SAR information from large compound sets.
Computational data mining and visualization techniques play a central part in the extraction of structure-activity relationship (SAR) information from compound sets including high-throughput screening data. Standard statistical and classification techniques can be used to organize data sets and evaluate the chemical neighborhood of potent hits; however, such methods are limited in their ability to extract complex SAR patterns from data sets and make them readily accessible to medicinal chemists. Therefore, new approaches and data structures are being developed that explicitly focus on molecular structure and its relationship to biological activity across multiple targets. Here, we review standard techniques for compound data analysis and describe new data structures and computational tools for SAR mining of large compound data sets.
Wawer
Mathias
M
Department of Life Science Informatics, Rheinische Friedrich-Wilhelms-Universität, D-53113 Bonn, Germany.
Lounkine
Eugen
E
Wassermann
Anne M
AM
Bajorath
Jürgen
J
D020650:Combinatorial Chemistry Techniques / Q000379:methods; D019295:Computational Biology / Q000379:methods*; D057225:Data Mining / Q000379:methods*; D016503:Drug Delivery Systems; D015195:Drug Design; D006801:Humans; D004364:Pharmaceutical Preparations / Q000737:chemistry; D013329:Structure-Activity Relationship
D004364:Pharmaceutical Preparations