Once the JT-VAE model is optimized, JAEGER generates novel active molecules by taking a seed molecule as a starting point. JAEGER samples deterministically neighbors around the starting point by defining a number of principal axes around the starting point in both the tree and graph latent 28-D sub-spaces (Figure ). The direction and magnitude of these principal axes are defined proportionately to the eigenvectors and eigenvalues of the covariance matrices of the tree and graph latent vectors of the molecule training collection, respectively. In both tree and graph sub-spaces, JAEGER samples positions along each axis at intervals defined proportionately to the magnitude of the axis (see Methods). Samples in the tree and graph sub-spaces are combined resulting in 56-D vectors that are passed onto the activity model to predict the pIC50 values of the joint samples. Only joint samples predicted to have a pIC50 above a certain threshold are selected for decoding.