Machine learning algorithms for the prediction of hERG and CYP450 binding in drug development

Expert Opinion on Drug Metabolism and Toxicology

Volumn 6, Issue 7, 2010, Pages 821-833

  Klon, Anthony E ^a  

^a Ansaris   (United States)

Author keywords

CYP; HERG; Machine learning; Metabolism; Torsade de pointes; Toxicity

Indexed keywords

3,4 DIHYDROXYTAMOXIFEN; 4 HYDROXYTAMOXIFEN; ANTIFUNGAL AGENT; CYTOCHROME P450; CYTOCHROME P450 1A2; CYTOCHROME P450 2C19; CYTOCHROME P450 2C9; CYTOCHROME P450 2D6; CYTOCHROME P450 3A4; DRUG METABOLITE; ENDOXIFEN; NORTAMOXIFEN; POTASSIUM CHANNEL HERG; PREGNANE X RECEPTOR; TAMOXIFEN; UNCLASSIFIED DRUG;

ALGORITHM; ARTIFICIAL NEURAL NETWORK; BINDING AFFINITY; CHANNEL GATING; COMPUTER MODEL; COMPUTER PROGRAM; DRUG PROTEIN BINDING; DRUG SAFETY; DRUG SCREENING; ENZYME INHIBITOR INTERACTION; ENZYME SUBSTRATE COMPLEX; HUMAN; MACHINE LEARNING; NONHUMAN; PREDICTION; QT INTERVAL; QUANTITATIVE STRUCTURE ACTIVITY RELATION; REVIEW; SUPPORT VECTOR MACHINE; TORSADE DES POINTES; TOXICOKINETICS;

ALGORITHMS; ANIMALS; ARTIFICIAL INTELLIGENCE; CYTOCHROME P-450 ENZYME SYSTEM; DRUG DISCOVERY; ETHER-A-GO-GO POTASSIUM CHANNELS; FORECASTING; HUMANS; ISOENZYMES; PROTEIN BINDING; PROTEIN ISOFORMS;

EID: 77953956128     PISSN: 17425255     EISSN: None     Source Type: Journal    
DOI: 10.1517/17425255.2010.489550     Document Type: Review

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