In Silico Prediction of Physicochemical Properties of Environmental Chemicals Using Molecular Fingerprints and Machine Learning

Journal of Chemical Information and Modeling

Volumn 57, Issue 1, 2017, Pages 36-49

  Zang, Qingda ^a   Mansouri, Kamel ^b   Williams, Antony J ^b   Judson, Richard S ^b   Allen, David G ^a   Casey, Warren M ^c   Kleinstreuer, Nicole C ^c  

^a INTEGRATED LABORATORY SYSTEMS   (United States)

^b U S ENVIRONMENTAL PROTECTION AGENCY   (United States)

^c NATIONAL INSTITUTE OF ENVIRONMENTAL HEALTH SCIENCES   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CHEMICALS; ENVIRONMENTAL PROTECTION AGENCY; FORECASTING; MACHINE LEARNING; PREDICTIVE ANALYTICS; TOXICITY;

BIO-CONCENTRATION FACTORS; COEFFICIENT OF DETERMINATION; COMPUTATIONALLY EFFICIENT; ENVIRONMENTAL CHEMICALS; HIGH-THROUGHPUT SCREENING; OCTANOL-WATER PARTITION COEFFICIENT; QUANTITATIVE STRUCTURE PROPERTY RELATIONSHIPS; U.S. ENVIRONMENTAL PROTECTION AGENCY;

PHYSICOCHEMICAL PROPERTIES;

POLLUTANT; WATER;

CHEMICAL PHENOMENA; CHEMISTRY; COMPUTER SIMULATION; INFORMATION SCIENCE; MACHINE LEARNING; POLLUTANT; QUANTITATIVE STRUCTURE ACTIVITY RELATION; SOLUBILITY; TOXICITY; TRANSITION TEMPERATURE; VAPOR PRESSURE;

CHEMICAL PHENOMENA; COMPUTER SIMULATION; ENVIRONMENTAL POLLUTANTS; INFORMATICS; MACHINE LEARNING; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP; SOLUBILITY; TRANSITION TEMPERATURE; VAPOR PRESSURE; WATER;

EID: 85014132566     PISSN: 15499596     EISSN: 1549960X     Source Type: Journal    
DOI: 10.1021/acs.jcim.6b00625     Document Type: Article

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