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Kinase‐Kernel Models: Accurate Chemogenomic Method for the Entire Human Kinome
Author(s) -
Tian Li,
Mukherjee Prasenjit,
Martin Eric
Publication year - 2013
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.201300091
Subject(s) - kinome , computer science , kernel (algebra) , virtual screening , computational biology , quantitative structure–activity relationship , artificial intelligence , cheminformatics , machine learning , data mining , drug discovery , pattern recognition (psychology) , kinase , bioinformatics , chemistry , biology , mathematics , biochemistry , combinatorics
Chemogenomic kinase‐kernel virtual screening models interpolate between very accurate, empirically‐trained Profile‐QSAR models of the nearest binding‐site homologues with IC 50 assay data. Between them, activity has been predicted for the entire human kinome.