title: Robust modelling of solubility in supercritical carbon dioxide using Bayesian methods.
abstract: Two sparse Bayesian methods were used to derive predictive models of solubility of organic dyes and polycyclic aromatic compounds in supercritical carbon dioxide (scCO(2)), over a wide range of temperatures (285.9-423.2K) and pressures (60-1400 bar): a multiple linear regression employing an expectation maximization algorithm and a sparse prior (MLREM) method and a non-linear Bayesian Regularized Artificial Neural Network with a Laplacian Prior (BRANNLP). A randomly selected test set was used to estimate the predictive ability of the models. The MLREM method resulted in a model of similar predictivity to the less sparse MLR method, while the non-linear BRANNLP method created models of substantially better predictivity than either the MLREM or MLR based models. The BRANNLP method simultaneously generated context-relevant subsets of descriptors and a robust, non-linear quantitative structure-property relationship (QSPR) model for the compound solubility in scCO(2). The differences between linear and non-linear descriptor selection methods are discussed.
authors.affiliation: CSIRO Molecular & Health Technologies, Private Bag 10, Clayton South MDC, Clayton, Victoria 3168, Australia.
authors.forename: Anna
authors.initials: A
authors.lastname: Tarasova
authors.forename: Frank
authors.initials: F
authors.lastname: Burden
authors.forename: Johann
authors.initials: J
authors.lastname: Gasteiger
authors.forename: David A
authors.initials: DA
authors.lastname: Winkler
mesh_terms: D001499:Bayes Theorem; D002245:Carbon Dioxide / Q000737:chemistry*; D008956:Models, Chemical; D021281:Quantitative Structure-Activity Relationship; D012995:Solubility
chemical_list: D002245:Carbon Dioxide