NGT starts by training a CSLVAE . CSLVAE maps molecular graphs x ∈ X into a learned ddimensional latent space with an encoder q ϕ (z|x) parameterized by a graph neural network with parameters ϕ (Figure ). Given a query vector z ∈ R d , the decoder p θ (x|z, D) with parameters θ efficiently retrieves compounds from the CSL D. The CSLVAE decoder was designed to guarantee Fig. The NeuralGenThesis (NGT) generative AI over Combinatorial Synthesis Libraries. A) NGT starts by training a CSLVAE, which is comprised of (i) an encoder q ϕ (z|x), parameterized as a graph neural network, mapping molecular graphs, x ∈ X, into a learned d-dimensional latent space, and (ii) a specially architected decoder p θ (x|z, D), which efficiently retrieves compounds from a CSL, D, given a query vector, z ∈ R d . The architecture of the decoder thereby ensures that all compounds generated by CSLVAE are contained in the library and acts as a "neural database" for the CSL, permitting probabilistic access to compounds in the library through queries in the latent space. CSLVAE is trained end-to-end with backpropagation. B) NGT subsequently trains a policy πη(z), parameterized as a normalizing flow, to identify positions in the latent space which, when decoded with the trained CSLVAE decoder p θ (x|z, D), retrieve compounds from the CSL that satisfy a set of user-specified properties. C) Once trained, we can sample latent codes from the distribution induced by the policy to find compounds that meet the desired properties. The shaded regions indicate regions in chemical space with high probability to satisfy user-specified properties. that all compounds it generates are contained in the library. CSLVAE thereby permits probabilistic access to compounds in the CSL through queries in the latent space, encoding the CSL as a neural database. CSLVAE is trained end-to-end with backpropagation.