A hybrid mixture discriminant analysis-random forest computational model for the prediction of volume of distribution of drugs in human.

@article{Lombardo2006AHM,
  title={A hybrid mixture discriminant analysis-random forest computational model for the prediction of volume of distribution of drugs in human.},
  author={Franco Lombardo and R Scott Obach and Frank M Dicapua and Gregory A. Bakken and Jing Lu and David M. Potter and Feng Gao and Michael D. Miller and Yao Zhang},
  journal={Journal of medicinal chemistry},
  year={2006},
  volume={49 7},
  pages={
          2262-7
        }
}
A computational approach is described that can predict the VD(ss) of new compounds in humans, with an accuracy of within 2-fold of the actual value. A dataset of VD values for 384 drugs in humans was used to train a hybrid mixture discriminant analysis-random forest (MDA-RF) model using 31 computed descriptors. Descriptors included terms describing lipophilicity, ionization, molecular volume, and various molecular fragments. For a test set of 23 proprietary compounds not used in model… CONTINUE READING

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