Application of Self-Organizing Maps in Compounds Pattern Recognition and Combinatorial Library Design

Title: Application of Self-Organizing Maps in Compounds Pattern Recognition and Combinatorial Library Design

Volume: 9 Issue: 6

Author(s): Aixia Yan

Affiliation:

Keywords: Neural networks, Kohonen self-organizing map (SOM), compounds pattern recognition, combinatorial libraries comparison, combinatorial library design, drug-like, nondrug-like

Abstract: 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.

Cite this article as:

Yan Aixia, Application of Self-Organizing Maps in Compounds Pattern Recognition and Combinatorial Library Design, Combinatorial Chemistry & High Throughput Screening 2006; 9 (6) . https://dx.doi.org/10.2174/138620706777698562