Two models were developed incorporating compound IC50 data: a graph convolutional model and a neural network model based on Simplex descriptors. To develop the graph model, standardized SMILES strings for each of the constituent compounds were converted into graphs (adjacency matrices and node feature matrices) using the OpenChem software package . A combined mixture graph was created by taking the direct sum of individual adjacency matrices and the concatenation of node feature matrices. This combined graph was passed through a graph convolutional network resulting in a latent representation of the mixture graph, to which the respective Log IC50 values of each component compound were concatenated. The resulting representation was passed further through a neural network, and a classification score was produced as the final output.