  In the pharmaceutical industry, where it is common to generate many QSAR
models with large numbers of molecules and descriptors, the best QSAR methods
are those that can generate the most accurate predictions but that are also
insensitive to hyperparameters and are computationally efficient. Here we
compare Light Gradient Boosting Machine (LightGBM) to random forest,
single-task deep neural nets, and Extreme Gradient Boosting (XGBoost) on 30
in-house data sets. While any boosting algorithm has many adjustable
hyperparameters, we can define a set of standard hyperparameters at which
LightGBM makes predictions about as accurate as single-task deep neural nets,
but is a factor of 1000-fold faster than random forest and ~4-fold faster than
XGBoost in terms of total computational time for the largest models. Another
very useful feature of LightGBM is that it includes a native method for
estimating prediction intervals.
