Random Forest Models To Predict Aqueous Solubility
journal contributionposted on 2007-01-22, 00:00 authored by David S. Palmer, Noel M. O'Boyle, Robert C. Glen, John B. O. Mitchell
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 °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.
Read the peer-reviewed publication
regression analysisdescriptor selectionprediction0.69 log S unitsSupport Vector Machines330 moleculesPredict Aqueous SolubilityRandom Forest regressionRMSERFchemical spacelog molar solubilitydescriptor importanceRandom Forest regression modelSVMRandom Forest Modelsr 2PLSQSPR modelsANNmethodMDL drug data reportArtificial Neural Networkstest sets