Predicting human liver microsomal stability with machine learning techniques

Journal of Molecular Graphics and Modelling

Volumn 26, Issue 6, 2008, Pages 907-915

  Sakiyama, Yojiro ^a   Yuki, Hitomi ^a   Moriya, Takashi ^a   Hattori, Kazunari ^a   Suzuki, Misaki ^a   Shimada, Kaoru ^a   Honma, Teruki ^a  

^a PFIZER INC   (United States)

Author keywords

ADMET; In silico; Machine learning; Metabolic stability; Random forest

Indexed keywords

BIOLOGICAL ORGANS; COMPUTER SIMULATION; LOGISTICS; MATHEMATICAL MODELS; METABOLISM; REGRESSION ANALYSIS;

CHEMICAL STRUCTURES; HUMAN LIVER; METABOLIC STABILITY; MICROSOMAL STABILITY; RANDOM FOREST;

LEARNING SYSTEMS;

ARTICLE; DRUG ABSORPTION; DRUG DISTRIBUTION; DRUG ELIMINATION; DRUG METABOLISM; DRUG SCREENING; DRUG STABILITY; DRUG SYNTHESIS; HIGH THROUGHPUT SCREENING; LIVER MICROSOME; LOGISTIC REGRESSION ANALYSIS; MACHINE LEARNING; PRIORITY JOURNAL; RANDOM FOREST; SENSITIVITY AND SPECIFICITY; SUPPORT VECTOR MACHINE; TOXICITY TESTING;

ARTIFICIAL INTELLIGENCE; COMPUTER SIMULATION; DRUG EVALUATION, PRECLINICAL; DRUG STABILITY; HUMANS; LOGISTIC MODELS; MICROSOMES, LIVER; PREDICTIVE VALUE OF TESTS; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 37349114485     PISSN: 10933263     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jmgm.2007.06.005     Document Type: Article

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