Artificial intelligence and data mining for toxicity prediction

Current Computer-Aided Drug Design

Volumn 2, Issue 2, 2006, Pages 123-133

  Helma, Christoph ^a   Kazius, Jeroen ^b  

^a In Silico Toxicology   (Germany)

^b LEIDEN UNIVERSITY   (Netherlands)

Author keywords

Artificial intelligence; Chemoinformatics; Data mining; Data driven learning; Machine learning; Pattern recognition; Predictive toxicology; QSAR

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTATIONAL CHEMISTRY; LEARNING SYSTEMS; PATTERN RECOGNITION; TOXICITY;

CHEMICAL FEATURES; CHEMOINFORMATICS; DATA DRIVEN; DATA MINING ALGORITHM; DATA-DRIVEN LEARNING; MACHINE-LEARNING; PREDICTIVE TOXICOLOGY; QSAR; QUANTITATIVE STRUCTURE ACTIVITY RELATIONSHIP; TOXICITY PREDICTIONS;

DATA MINING;

DRUG;

ALGORITHM; ARTIFICIAL INTELLIGENCE; ARTIFICIAL NEURAL NETWORK; BAYES THEOREM; CALCULATION; CORRELATION ANALYSIS; DRUG ACTIVITY; DRUG IDENTIFICATION; EXPERT SYSTEM; INFORMATION PROCESSING; MULTIPLE LINEAR REGRESSION ANALYSIS; PARTITION COEFFICIENT; PRACTICE GUIDELINE; PREDICTION; PRINCIPAL COMPONENT ANALYSIS; QUANTITATIVE STRUCTURE ACTIVITY RELATION; REPRODUCIBILITY; REVIEW; VALIDATION PROCESS;

EID: 33746206546     PISSN: 15734099     EISSN: None     Source Type: Journal    
DOI: 10.2174/157340906777441717     Document Type: Review

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