Developing a model to predict permeability that could save significant resources for a pharmaceutical company, while at the same time more rapidly identifying molecules that have a sufficient permeability to become a drug:

(a) Start R and use these commands to load the data: > library(AppliedPredictiveModeling) > data(permeability) The matrix fingerprints contains the 1,107 binary molecular predictors for the 165 compounds, while permeability contains permeability response.

(b) The fingerprint predictors indicate the presence or absence of substructures of a molecule and are often sparse meaning that relatively few of the molecules contain each substructure. Filter out the predictors that have low frequencies using the nearZeroVar function from the caret package. How many predictors are left for modeling?

(c) Split the data into a training and a test set, pre-process the data, and tune a PLS model. How many latent variables are optimal and what is the corresponding resampled estimate of R2?

(d) Predict the response for the test set. What is the test set estimate of R2?

(e) Try building other models discussed in this chapter. Do any have better predictive performance?

(f) Would you recommend any of your models to replace the permeability laboratory experiment?