Graph kernels for chemical compounds using topological and three-dimensional local atom pair environments

Neurocomputing

Volumn 74, Issue 1-3, 2010, Pages 219-229

  Hinselmann, Georg ^a   Fechner, Nikolas ^a   Jahn, Andreas ^a   Eckert, Matthias ^a   Zell, Andreas ^a  

^a UNIVERSITY OF TÜBINGEN   (Germany)

Author keywords

Cheminformatics; Graph kernel; Molecular graph; Structure activity relationship; Support vector machine

Indexed keywords

BIOACTIVITY; GRAPH THEORY; LEARNING SYSTEMS; MATRIX ALGEBRA; MOLECULAR STRUCTURE; SUPPORT VECTOR MACHINES; TABLE LOOKUP;

CHEMINFORMATICS; GEOMETRICAL DISTANCES; GRAPH KERNELS; KERNEL FUNCTION; MACHINE-LEARNING; MOLECULAR GRAPHS; PROPERTY; SHORTEST PATH MATRIX; STRUCTURE-ACTIVITY RELATIONSHIPS; SUPPORT VECTORS MACHINE;

ATOMS;

CHEMICAL COMPOUND;

ARTICLE; ATOM; CHEMICAL ANALYSIS; CLASSIFICATION; CONTROLLED STUDY; INTERMETHOD COMPARISON; KERNEL METHOD; LEARNING; MATHEMATICAL ANALYSIS; MATHEMATICAL COMPUTING; MATHEMATICAL PARAMETERS; PRIORITY JOURNAL; REGRESSION ANALYSIS; STRUCTURE ACTIVITY RELATION; SUPPORT VECTOR MACHINE;

EID: 78649487672     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neucom.2010.03.008     Document Type: Article

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