Robust modelling of solubility in supercritical carbon dioxide using Bayesian methods

Journal of Molecular Graphics and Modelling

Volumn 28, Issue 7, 2010, Pages 593-597

  Tarasova, Anna ^a   Burden, Frank ^a   Gasteiger, Johann ^b   Winkler, David A ^a  

^a CSIRO   (Australia)

^b Molecular Networks GmbH   (Germany)

Author keywords

Bayesian methods; Dyes; Solubility; Structure property relationships; Supercritical carbon dioxide

Indexed keywords

A-LAPLACIAN; ARTIFICIAL NEURAL NETWORK; BAYESIAN; BAYESIAN METHODS; DESCRIPTORS; EXPECTATION-MAXIMIZATION ALGORITHMS; MULTIPLE LINEAR REGRESSIONS; NON-LINEAR; ORGANIC DYE; POLYCYCLIC AROMATIC COMPOUNDS; PREDICTIVE ABILITIES; PREDICTIVE MODELS; PREDICTIVITY; QUANTITATIVE STRUCTURE-PROPERTY RELATIONSHIP MODELS; SELECTION METHODS; SPARSE PRIOR; STRUCTURE PROPERTY RELATIONSHIPS; SUPERCRITICAL CARBON DIOXIDES; TEST SETS;

AROMATIC COMPOUNDS; BAYESIAN NETWORKS; CARBON DIOXIDE; LINEAR REGRESSION; NEURAL NETWORKS; SOLUBILITY; SULFUR COMPOUNDS; TERNARY SYSTEMS;

SUPERCRITICAL FLUID EXTRACTION;

CARBON DIOXIDE;

ARTICLE; ARTIFICIAL NEURAL NETWORK; BAYES THEOREM; CONTROLLED STUDY; HYDROPHOBICITY; PREDICTIVE VALIDITY; PRIORITY JOURNAL; QUANTITATIVE STRUCTURE PROPERTY RELATION; SOLUBILITY;

BAYES THEOREM; CARBON DIOXIDE; MODELS, CHEMICAL; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP; SOLUBILITY;

EID: 76749124899     PISSN: 10933263     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jmgm.2009.12.004     Document Type: Article

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