For the neural network model, mixture Simplex descriptors were first produced and then cleaned, removing the highly correlated simplexes. The resulting Simplex descriptors were passed through several layers of a neural network, after which the Log IC50 values of each component compound were appended to the mixture representation. Like the graph model, the resulting representation was used as an input to the neural network model, and a classification score was produced as output. Each model was defined as above up to a set of hyperparameters (number of layers, size of each layer, learning rate, number of training epochs, etc.). To find optimal hyperparameters for each model, a specific mixture-oriented external validation was performed on several models. For a wide range of hyperparameter values, only insignificant differences were observed between models. Therefore, within this range, the final models were chosen for computational efficiency.