Kohonen-féle önszervezo{combining double acute accent}do{combining double acute accent} tulajdonságtérkép használata humán ADMET és kináz adatok QSAR predikciójában

Translated title of the contribution: Application of kohonen self organized feature maps in QSAR of human ADMET and kinase data sets

Bálint Hegymegi-Barakonyi, Laśzló Orfi, Gÿorgý Kéri, Ístvan Es̈ Kovesdi

Research output: Contribution to journalReview article

1 Citation (Scopus)

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.

Translated title of the contributionApplication of kohonen self organized feature maps in QSAR of human ADMET and kinase data sets
Original languageHungarian
Pages (from-to)143-148
Number of pages6
JournalActa pharmaceutica Hungarica
Volume83
Issue number4
Publication statusPublished - Dec 1 2013

ASJC Scopus subject areas

  • Pharmaceutical Science

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