Random Forest Models To Predict Aqueous Solubility

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Random forest regression and spectral band selection for estimating sugarcane leaf nitrogen concentration using EO-1 Hyperion hyperspectral data (2013, methodology): “In each tree, the ensemble predicts the data that are not in the tree (the out-of-bag: OOB data, approximately 30%), and by calculating the difference in the mean square errors between the OOB data and data used to grow the regression trees, the RF algorithm gives an error of prediction called the O”
ToxicBlend: Virtual Screening of Toxic Compounds with Ensemble Predictors (2018, background): “One such project is the ToxCast project [2] supported by the U.S. Environmental Protection Agency (EPA), which oﬀers the scientiﬁc community unprecedented datasets of observed in vitro interactions between thousands of small molecules and known toxicity-related targets.”
In silico modelling of permeation enhancement potency in Caco-2 monolayers based on molecular descriptors and random forest. (2015, methodology): “, QSAR models have been developed for permeability [2] and solubility [3–5].”
Accurate and efficient target prediction using a potency-sensitive influence-relevance voter (2015, positive): “RFs have been applied in chemoinformatics to QSAR/ QSPR modeling, and molecular classification problems [42–44].”