A maximum common substructure-based algorithm for searching and predicting drug-like compounds

Bioinformatics

Volumn 24, Issue 13, 2008, Pages

  Cao, Yiqun ^a   Jiang, Tao ^a   Girke, Thomas ^b  

^a University of California Riverside   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ANTINEOPLASTIC AGENT; ANTIVIRUS AGENT;

ANTINEOPLASTIC ACTIVITY; ANTIVIRAL ACTIVITY; ARTICLE; BREAST TUMOR; CHEMICAL ANALYSIS; CHEMICAL STRUCTURE; CLUSTER ANALYSIS; COMPARATIVE MOLECULAR SIMILARITY INDICES ANALYSIS; COMPUTER MODEL; DRUG DETERMINATION; HIGH THROUGHPUT SCREENING; HUMAN; HUMAN CELL; MATHEMATICAL COMPUTING; MAXIMUM COMMON SUBSTRUCTURE; PREDICTION; PRIORITY JOURNAL; SENSITIVITY AND SPECIFICITY; SUPPORT VECTOR MACHINE; SYSTEM ANALYSIS; TUMOR CELL LINE;

BIOACTIVITY; DRUG PRODUCTS; INTELLIGENT SYSTEMS; MOLECULAR BIOLOGY; SUPPORT VECTOR MACHINES;

BACKTRACKING ALGORITHM; BIOACTIVE COMPOUNDS; BIOLOGICALLY ACTIVE COMPOUNDS; GLOBAL SIMILARITY; HIGH-THROUGHPUT SCREENING; SIMILARITY MEASURE; STRUCTURAL SIMILARITY; SUPPORT VECTOR MACHINE (SVMS);

FORECASTING;

EID: 46249123633     PISSN: 13674803     EISSN: 13674811     Source Type: Journal    
DOI: 10.1093/bioinformatics/btn186     Document Type: Article

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