A total of 15 model types trained purely on gamma binary labels were developed for use in consensus. Two descriptor types (RDKit descriptors and Morgan Fingerprint) were calculated for individual compounds in training mixtures and composited by either element-wise average or sum at every position in the descriptor. A third descriptor type, Simplex , was calculated for the training mixtures, and did not require composition. Each of these descriptor/composition pairs was used as input for neural network, random forest, and gradient boosting models. To calculate a consensus, the prediction of every model for every descriptor was averaged. This resulted in three consensus models, each corresponding to a different descriptor.