R/permeability_qsar.R

In modeldata: Data Sets Useful for Modeling Examples

#' Predicting permeability from chemical information
#'
#' @description
#' A quantitative structure-activity relationship (QSAR) data set to predict
#' when a molecule can permeate cells.
#'
#' @name permeability_qsar
#' @aliases permeability_qsar
#' @docType data
#' @return \item{permeability_qsar}{a data frame}
#'
#' @details
#' This pharmaceutical data set was used to develop a model for predicting
#' compounds' permeability. In short, permeability is the measure of a
#' molecule's ability to cross a membrane. The body, for example, has notable
#' membranes between the body and brain, known as the blood-brain barrier, and
#' between the gut and body in the intestines. These membranes help the body
#' guard critical regions from receiving undesirable or detrimental substances.
#' For an orally taken drug to be effective in the brain, it first must pass
#' through the intestinal wall and then must pass through the blood-brain
#' barrier in order to be present for the desired neurological target.
#' Therefore, a compound's ability to permeate relevant biological membranes
#' is critically important to understand early in the drug discovery process.
#' Compounds that appear to be effective for a particular disease in research
#' screening experiments, but appear to be poorly permeable may need to be
#' altered in order improve permeability, and thus the compound's ability to
#' reach the desired target. Identifying permeability problems can help guide
#' chemists towards better molecules.
#'
#' Permeability assays such as PAMPA and Caco-2 have been developed to help
#' measure compounds' permeability (Kansy et al, 1998). These screens are
#' effective at quantifying a compound's permeability, but the assay is
#' expensive labor intensive. Given a sufficient number of compounds that have
#' been screened, we could develop a predictive model for permeability in an
#' attempt to potentially reduce the need for the assay. In this project there
#' were 165 unique compounds; 1107 molecular fingerprints were determined for
#' each. A molecular fingerprint is a binary sequence of numbers that
#' represents the presence or absence of a specific molecular sub-structure.
#' The response is highly skewed, the predictors are sparse (15.5% are present),
#' and many predictors are strongly associated.
#'
#' Columns:
#' \itemize{
#'   \item \code{permeability}: numeric
#'   \item \code{chem_fp_0001} - \code{chem_fp_1107}: numeric
#' }
#'
#' @source
#' Kuhn, Max, and Kjell Johnson. _Applied predictive modeling_. New York:
#' Springer, 2013.
#'
#' @examples
#' data(permeability_qsar)
#' str(permeability_qsar)
#'
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