The growing number of available experimental data and the advancement of computing power have enabled the application of deep learning (DL) to molecular discovery. Among DL methods, deep generative neural network (DGNN) has emerged as a leading technique to find promising candidate molecules. By studying the distribution of the input data (experimental, computational, or both), DGNN samples from the distribution generated by the NN model to create novel molecules. By applying property constraints during model training, we guide the DGNN model to generate molecules with the desired properties. Particularly, we apply semi-supervised DGNN to find better organic light emitting diodes (OLED) for display applications and catalysts for selective oxidation of CH₄ to CH₃OH, H₂O to O₂, and NH₃ to N₂.