For optimization, we use the ADAM algorithm 23 with a base learning rate of 0.003. The learning rate is modulated using an exponential decay function with a multiplicative factor of 0.9. The number of molecules in each training mini-batch is set to 8. Optimization proceeds in two stages: in the first stage, the beta parameter is set to zero. In the second stage, the beta parameter is set to 0.005 and the ADAM optimizer is reset with a base learning rate of 0.0003. Each stage consists of 36 epochs, for a total of 72 optimization epochs. We used the PyTorch implementation provided in the original article 16 as a starting point for our algorithmic developments. We optimized the model parameters using an NVIDIA Tesla K80 GPU with 11.5 GB of memory. For the malaria model, Where c = -Nc, -Nc + 1, …, 0, …, Nc -1, Nc; where ltree,i is the eigenvalue associated with the eigenvector etree,i; and where g is a scaling factor. For a given number of search axes Naxes,tree, the total number of unique neighbors together with the original seed tree latent representation is Nsamples,tree = Naxes,tree (2Nc + 1) -Naxes,tree + 1. We use the same sampling scheme in the graph latent space to obtain Nsamples,graph. By combining all Nsamples,tree tree samples with all Nsamples,graph graph samples, we obtain a total of Nsamples = Nsamples,tree Nsamples,graph -1 unique samples in latent space. Each of those 56-D latent samples is passed through the activity model to predict their pIC50 values. Only samples with pIC50 values above a certain threshold are decoded onto a corresponding molecule. On our hardware, it takes about 2.5 s to decode a latent vector onto a molecule. This deterministic sampling scheme thus ensures that JAEGER is able to generate relatively diverse active molecules while compensating for the relatively slow decoding time of the JT-VAE model.