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Cited by 364

The DOI matches its Crossref record; 28 of the 28 references it cites were found in a registry or an index.

Random Forest Models To Predict Aqueous Solubility

Palmer, David S., O'Boyle, Noel M., Glen, Robert C., Mitchell, John B. O.·2007·Journal of Chemical Information and Modeling
DOI 10.1021/ci060164k
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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.

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Metadata

Authors
Palmer, D. S., O'Boyle, N. M., Glen, R. C., & Mitchell, J. B. O.
Venue
Journal of Chemical Information and Modeling
Publisher
American Chemical Society (ACS)
Published
2006-12-02
Volume
47
Issue
1
Pages
150-158
ISSN
  • 1549-9596 (ISSN-L)
  • 1549-960X
DOI
10.1021/ci060164k
OpenAlex
W2041392558
Cited by
364
References
28
FWCI
6.15
Type
journal article
Language
en
Open access
closed

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