Using Density Functional Theory (DFT) computational methods, it is possible to calculate HOMO energies of photovoltaic materials , yet these computations are costly in resources and time. In contrast, Machine Learning techniques allow fast and accurate prediction of HOMO levels. A comprehensive and detailed review of various machine learning techniques applied for the prediction of molecular orbital characteristics is given in . In particular, Kernel Ridge Regression (KRR) is used in , , , , , Gaussian process regression (GPR), linear regressions (Elastic Net, Bayesian Ridge Regression) and Random Forest (RF) are applied in , , , respectively. Deep Learning techniques were used in Collins CR , Unke and Meuwly [2019], , , , , Unke and Meuwly , including those based on graph molecular representations , . The previously developed models set the regression task as the principal goal. To the best of our knowledge, no generation model for the molecules with the given HOMO level has been developed yet. Our technique is based on the junction tree variational autoencoder (JT VAE) architecture which uses graph molecular representations as a reliable way of reproducing chemically valid structures. Our model achieves state-of-the-art results in HOMO energy prediction and allows the generation of new molecules with desired HOMO value. Several strategies for discovering chemical structures in the embedding space were suggested and explored.