Quantitative prediction of regioselectivity toward cytochrome P450/3A4 using machine learning approaches

Molecular Informatics

Volumn 29, Issue 3, 2010, Pages 243-249

  Hasegawa, Kiyoshi ^a   Koyama, Michio ^b   Funatsu, Kimito ^b  

^a CHUGAI PHARMACEUTICAL CO LTD   (Japan)

^b UNIVERSITY OF TOKYO   (Japan)

Author keywords

ADMET; CYP 3A4; Random forest; Regioselectivity; Structure activity relationships

Indexed keywords

ARTIFICIAL INTELLIGENCE; BIOMOLECULES; FORECASTING; LEARNING SYSTEMS; METABOLITES;

ADMET; CYTOCHROME P450-3A4; DRUG CANDIDATES; DRUG DISCOVERY PROCESS; MACHINE LEARNING APPROACHES; PREDICTION OF REGIOSELECTIVITY; PROPERTY; QUANTITATIVE PREDICTION; RANDOM FORESTS; STRUCTURE-ACTIVITY RELATIONSHIPS;

REGIOSELECTIVITY;

ALFENTANIL; AMIODARONE; AZELASTINE; BUPRENORPHINE; CISAPRIDE; CITALOPRAM; CLARITHROMYCIN; CLOZAPINE; CODEINE; COLCHICINE; CYTOCHROME P450 3A4; DEXAMETHASONE; DEXTROMETHORPHAN; DIAZEPAM; DILTIAZEM; DOCETAXEL; DRUG METABOLITE; EBASTINE; ERYTHROMYCIN; ESTRONE; ETOPOSIDE; FENTANYL; FLUNITRAZEPAM; HALOFANTRINE; HALOPERIDOL; HYDROCORTISONE; INDINAVIR; MIDAZOLAM; NEVIRAPINE; UNINDEXED DRUG;

ACCURACY; ANALYTIC METHOD; ARTICLE; DEALKYLATION; DRUG HYDROXYLATION; ENZYME METABOLISM; HYDROGEN BOND; K NEAREST NEIGHBOR; MACHINE LEARNING; PHARMACOPHORE; PREDICTION; PRIORITY JOURNAL; RANDOM FOREST; X RAY CRYSTAL STRUCTURE;

EID: 77952703842     PISSN: 18681743     EISSN: 18681751     Source Type: Journal    
DOI: 10.1002/minf.200900086     Document Type: Article

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