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Random forest models to predict aqueous solubility

Palmer, D. S. and O'Boyle, N. M. and Glen, R. C. and Mitchell, J. B. (2007) Random forest models to predict aqueous solubility. Journal of Chemical Information and Modeling, 47 (1). pp. 150-158.

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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.

Item type: Article
ID code: 39174
Keywords: aqueous solubility , forest models , random forest regression , Physics, Microbiology, Probabilities. Mathematical statistics, Chemical Engineering(all), Chemistry(all), Library and Information Sciences, Computer Science Applications
Subjects: Science > Physics
Science > Microbiology
Science > Mathematics > Probabilities. Mathematical statistics
Department: Faculty of Science > Physics
Faculty of Science > Mathematics and Statistics > Statistics and Modelling Science
Related URLs:
    Depositing user: Pure Administrator
    Date Deposited: 16 Apr 2012 12:22
    Last modified: 05 Sep 2014 15:46
    URI: http://strathprints.strath.ac.uk/id/eprint/39174

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