in which the logistic loss and the gradient are the weighted averages of losses and gradients on each receptor set, respectively, with weights being the number of compounds of each receptor set. The most robust for this particular use case proved to be Random Forest (RF), so we proceeded with that algorithm and created several classifier model variations.