Application of self-organizing maps in compounds pattern recognition and combinatorial library design.
In the computer-aided drug design, in order to find some new leads from a large library of compounds, the pattern recognition study of the diversity and similarity assessment of the chemical compounds is required; meanwhile in the combinatorial library design, more attention is given to design target focusing library along with diversity and drug-likeness criteria. This review presents the current state-of-art applications of Kohonen self-organizing maps (SOM) for studying the compounds pattern recognition, comparing the property of molecular surfaces, distinguishing drug-like and nondrug-like molecules, splitting a dataset into the proper training and test sets before constructing a QSAR (Quantitative Structural-Activity Relationship) model, and also for the combinatorial libraries comparison and the combinatorial library design. The Kohonen self-organizing map will continue to play an important role in drug discovery and library design.
Yan
Aixia
A
Department of Pharmaceutical Engineering, P.O. Box 53, Beijing University of Chemical Technology, 15 BeiSanHuan East Road, Beijing 100029, PR China. yanax@mail.buct.edu.cn
D003198:Computer Simulation; D015195:Drug Design; D016571:Neural Networks, Computer; D019151:Peptide Library; D021281:Quantitative Structure-Activity Relationship; D013499:Surface Properties
D019151:Peptide Library