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In the pharmaceutical industry, where it is common to generate many QSARmodels with large numbers of molecules and descriptors, the best QSAR methodsare those that can generate the most accurate predictions but that are alsoinsensitive to hyperparameters and are computationally efficient. Here wecompare Light Gradient Boosting Machine (LightGBM) to random forest,single-task deep neural nets, and Extreme Gradient Boosting (XGBoost) on 30in-house data sets. While any boosting algorithm has many adjustablehyperparameters, we can define a set of standard hyperparameters at whichLightGBM 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 thanXGBoost in terms of total computational time for the largest models. Anothervery useful feature of LightGBM is that it includes a native method forestimating prediction intervals.

Light Gradient Boosting Machine as a Regression Method for Quantitative Structure-Activity Relationships