Abstract：
:Random Forest regression (RF), Partial-Least-Squares (PLS) regression, Support Vector Machines (SVM), and Artificial Neural Networks (ANN) were used to develop QSPR models for the prediction of aqueous solubility, based on experimental data for 988 organic molecules. The Random Forest regression model predicted aqueous solubility more accurately than those created by PLS, SVM, and ANN and offered methods for automatic descriptor selection, an assessment of descriptor importance, and an in-parallel measure of predictive ability, all of which serve to recommend its use. The prediction of log molar solubility for an external test set of 330 molecules that are solid at 25 degrees C gave an r2 = 0.89 and RMSE = 0.69 log S units. For a standard data set selected from the literature, the model performed well with respect to other documented methods. Finally, the diversity of the training and test sets are compared to the chemical space occupied by molecules in the MDL drug data report, on the basis of molecular descriptors selected by the regression analysis.
journal_name
J Chem Inf Modeljournal_title
Journal of chemical information and modelingauthors
Palmer DS,O'Boyle NM,Glen RC,Mitchell JBdoi
10.1021/ci060164ksubject
Has Abstractpub_date
2007-01-01 00:00:00pages
150-8issue
1eissn
1549-9596issn
1549-960Xjournal_volume
47pub_type
杂志文章abstract：:The number of journal articles in the scientific domain has grown to the point where it has become impossible for researchers to capitalize on all findings in their relevant discipline. Information is stored in these articles in a number of ways, including figures that describe important results. In organic chemistry,...
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