In general, deep generative models can operate over large spaces of molecular structures and embed the chemical properties of these structures into a vector space. These models can generate new and previously unidentified chemical compounds by decoding from this 'latent' space of chemical structures. Recent reviews of the de novo design have examined progress in generative model architecture and evaluated their efficiency with reference to experimentally validated test cases in the literature .