[Application of Kohonen Self-Organizing Feature Maps in QSAR of human ADMET and kinase data sets].

Abstract

QSAR predictions have been proven very useful in a large number of studies for drug design, such as kinase inhibitor design as targets for cancer therapy, however the overall predictability often remains unsatisfactory. To improve predictability of ADMET features and kinase inhibitory data, we present a new method using Kohonen's Self-Organizing Feature Map (SOFM) to cluster molecules based on explanatory variables (X) and separate dissimilar ones. We calculated SOFM clusters for a large number of molecules with human ADMET and kinase inhibitory data, and we showed that chemically similar molecules were in the same SOFM cluster, and within such clusters the QSAR models had significantly better predictability. We used also target variables (Y, e.g. ADMET) jointly with X variables to create a novel type of clustering. With our method, cells of loosely coupled XY data could be identified and separated into different model building sets.

• Authors: Bálint Hegymegi-Barakonyi, László Orfi, György Kéri, István Kövesdi
• Journal: Acta pharmaceutica Hungarica (Acta Pharm Hung) Vol. 83 Issue 4 Pg. 143-8 ( 2013) ISSN: 0001-6659 [Print] Hungary
• Vernacular Title: Kohonen-féle önszerveződő tulajdonságtérkép használata humán ADMET és kináz adatok QSAR predikciójában.
• PMID: 24575660 (Publication Type: English Abstract, Journal Article)

Chemical References

• Protein Kinase Inhibitors

Topics

• Algorithms
• Cluster Analysis
• Computer Simulation
• Drug Design
• Humans
• Models, Molecular
• Molecular Structure
• Protein Kinase Inhibitors (chemistry, pharmacology)
• Quantitative Structure-Activity Relationship