This is an animation of a molecular dynamics trajectory of a hemoglobin. See https://www.rcsb.org/structure/5EUI for the original PDB file, and watch the animation above.
I predicted the aqueous solubility of chemical compounds listed in a public dataset using a basic linear model and an untuned random forest in my previous post. The random forest showed the two models’ best performance, achieving an RMSE of 0.866 and an R^2 of 0.828. In this post, I train an XGBoost model on the same data.
Load the libraries library(tidyverse) library(tidymodels) library(ranger) library(usemodels) library(gridExtra) library(vip) theme_set(theme_classic()) Load, split, and bootstrap sample the data.
