The JT-VAE model represents a molecule through both a junction tree, which represents the arrangement of sub-structures within a molecule, as well as a molecular graph. The tree and graph representations are each mapped onto separate 28-dimensional (28-D) vectors. Within JAEGER, we thus train a JT-VAE model to encode a single molecule onto two 28-D vector representations as well as decode those two vectors back onto a molecule (see Figure ). The collections of tree and graph vectors corresponding to the molecules in the training set span two 28-D continuous latent spaces that JAEGER explores to obtain novel active molecules. Exploration of those latent spaces is guided by an activity model predicting the activity of molecules in the assay of interest, given in terms of the negative logarithm of the half maximal inhibitory concentration (pIC50). The JT-VAE model is optimized jointly for the molecule encoding and decoding tasks as well as the activity prediction task to ensure that the latent representations support all of these tasks (see Methods).