The design of functional artificial biomolecules has been one of the main interests of biotechnology in recent years. The aim is to design sequences that have the same functionality of the natural ones and comparable features. Data-driven approaches are one of the more successful strategies. In Machine Learning, generative statistical models are tools to generate artificial biomolecular sequences. They are trained on Multiple Sequence Alignments of homologous families which consist of positive unlabelled sequences. In literature there are several examples where generative models have been built successfully to generate functional RNA and Proteins. Relying on maximum entropy principle, Direct Coupling Analysis (DCA) models are based on the Boltzmann Distribution in physics.