Fingerprint-based in silico models for the prediction of P-glycoprotein substrates and inhibitors

Bioorganic and Medicinal Chemistry

Volumn 20, Issue 18, 2012, Pages 5388-5395

  Poongavanam, Vasanthanathan ^a   Haider, Norbert ^a   Ecker, Gerhard F ^a  

^a UNIVERSITY OF VIENNA   (Austria)

Author keywords

ADMET; Association rule; Fingerprint; In silico; Machine learning methods; P glycoprotein

Indexed keywords

AMIODARONE; DILTIAZEM; GLYCOPROTEIN P; GLYCOPROTEIN P INHIBITOR; GP 062; INDINAVIR; KETOCONAZOLE; MEPACRINE; NELFINAVIR; NICARDIPINE; PAROXETINE; QUINIDINE; UNCLASSIFIED DRUG; VERAPAMIL;

ACCURACY; ARTICLE; BILAYER MEMBRANE; BINDING SITE; CHEMICAL ANALYSIS; CHEMICAL STRUCTURE; COMPUTER MODEL; CORRELATION COEFFICIENT; DATA MINING; DRUG ABSORPTION; DRUG DISTRIBUTION; DRUG EXCRETION; DRUG METABOLISM; DRUG TOXICITY; HYDROPHOBICITY; KAPPA NEAREST NEIGHBOR; LEARNING ALGORITHM; MACHINE LEARNING; PHARMACOPHORE; PHYSICAL CHEMISTRY; PRINCIPAL COMPONENT ANALYSIS; PROBABILITY; RANDOM FOREST; SENSITIVITY AND SPECIFICITY; SUPPORT VECTOR MACHINE; WRAPPER SUBSET EVALUATOR;

ALGORITHMS; COMPUTATIONAL BIOLOGY; DATABASES, PHARMACEUTICAL; DRUG SUBSTITUTION; MODELS, MOLECULAR; MOLECULAR STRUCTURE; P-GLYCOPROTEIN;

EID: 84866259721     PISSN: 09680896     EISSN: 14643391     Source Type: Journal    
DOI: 10.1016/j.bmc.2012.03.045     Document Type: Article

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