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The Importance of Descriptor‐Based Clusterization in QSAR Models Development: Tyrosine Kinases Inhibitors as a Key Study
Author(s) -
Marzaro Giovanni,
Tonus Francesca,
Brun Paola,
Castagliuolo Ignazio,
Guiotto Adriano,
Chilin Adriana
Publication year - 2011
Publication title -
molecular informatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.481
H-Index - 68
eISSN - 1868-1751
pISSN - 1868-1743
DOI - 10.1002/minf.201100036
Subject(s) - quantitative structure–activity relationship , virtual screening , key (lock) , computer science , drug discovery , computational biology , process (computing) , cheminformatics , chemistry , artificial intelligence , machine learning , data mining , computational chemistry , biology , biochemistry , computer security , operating system
Quantitative Structure Activity Relationship (QSAR) is a well known cheminformatic tool for the discovery of novel biologically active compounds. However, when large and heterogeneous datasets are mined, it is not possible to derive a QSAR equation able to predict in a satisfactory manner the activity of the compounds. Thus, QSAR models are often inadequate for virtual screening purpose. Herein we present a novel approach to multitarget classification QSAR models, useful to assess the selectivity profile of the tyrosine kinases inhibitors. A descriptor‐based clusterization process was employed, that allowed the generation of models with high accuracies and independent from the chemical classification of the compounds (i.e. from the scaffold type). The herein proposed methodology can lead to QSAR models useful for virtual screening processes.