Abstract: In various examples, systems and methods are disclosed relating to using machine learning models to generate small molecules with desired structural or physicochemical properties with high sampling efficiency. In some implementations, one or more processors receive a data structure representing a first small molecule and encode the data structure into a latent distribution of a fixed size using a machine learning model, thereby determining an encoded representation of the data structure. To generate new molecules with similar properties to the first small molecule, the processors apply noise to the encoded representation to determine a modified encoded representation. The modified encoded representation is decoded to determine a modified data structure representing a second small molecule different from the first small molecule.