The model represented by , is an integrated Variational Auto-Encoder (VAE) with a Convolutional Neural Network (CNN) followed by an attention mechanism. The VAE aids in learning the Drug-Protein Interactions (DPIs) through probabilistic evaluations that also reduce redundancies, while the CNN is to extract local features of drugs and protein, and the attention mechanism figures out the key features that relates to DPI sites. Case studies have proved the ability of this model to generate proper results compared to base models. SGVAE , a modified Grammar VAE (GVAE) by adding properties-information to the input data, resulting with a supervised environment. SGVAE covers two important small-molecules generation applications; properties prediction and custom novel molecules generation. Moreover, SGVAE has the ability to measure the properties' values of given molecules. SGVAE outperformed models that use SMILES for properties predicting tasks.