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Random Forest:  A Classification and Regression Tool for Compound Classification and QSAR Modeling

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Download Hi-Res ImageDownload to MS-PowerPointCite This:J. Chem. Inf. Comput. Sci. 2003, 43, 6, 1947-1958
    B: Liquids; Chemical and Dynamical Processes in Solution

    Random Forest:  A Classification and Regression Tool for Compound Classification and QSAR Modeling
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    Biometrics Research, Merck Research Laboratories, P.O. Box 2000, Rahway, New Jersey 07065, Molecular Systems, Merck Research Laboratories, P.O. Box 4, West Point, Pennsylvania 19486, and Molecular Systems, Merck Research Laboratories, P.O. Box 2000, Rahway, New Jersey 07065
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    Journal of Chemical Information and Computer Sciences

    Cite this: J. Chem. Inf. Comput. Sci. 2003, 43, 6, 1947–1958
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    https://doi.org/10.1021/ci034160g
    Published November 4, 2003
    Copyright © 2003 American Chemical Society
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    Abstract

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    A new classification and regression tool, Random Forest, is introduced and investigated for predicting a compound's quantitative or categorical biological activity based on a quantitative description of the compound's molecular structure. Random Forest is an ensemble of unpruned classification or regression trees created by using bootstrap samples of the training data and random feature selection in tree induction. Prediction is made by aggregating (majority vote or averaging) the predictions of the ensemble. We built predictive models for six cheminformatics data sets. Our analysis demonstrates that Random Forest is a powerful tool capable of delivering performance that is among the most accurate methods to date. We also present three additional features of Random Forest:  built-in performance assessment, a measure of relative importance of descriptors, and a measure of compound similarity that is weighted by the relative importance of descriptors. It is the combination of relatively high prediction accuracy and its collection of desired features that makes Random Forest uniquely suited for modeling in cheminformatics.

    Copyright © 2003 American Chemical Society

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     Corresponding author phone:  (732)594-5544; fax:  (732)594-1565; e-mail:  [email protected].

    †

     Biometrics Research.

    ‡

     Molecular Systems, West Point, Pennsylvania.

    §

     Molecular Systems, Rahway, New Jersey.

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    The R code used to generate most of the results in this paper is available as a text file, as are data files for the data sets for which we generated our own descriptors (P-gp, MDRR, and Dopamine). This material is available free of charge via the Internet at http://pubs.acs.org.

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    Cite this: J. Chem. Inf. Comput. Sci. 2003, 43, 6, 1947–1958
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    Published November 4, 2003
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