Oral diseases, including gingivitis and periodontitis, are linked to the Wnt signaling pathway, vital for bone metabolism, cementum homeostasis, and mesenchymal stem cell differentiation. Advances in generative AI techniques, such as variational autoencoders (VAEs) and quantum variational classifiers (QVCs), offer promising tools for predicting gene associations between drugs and diseases. This study aims to compare the predictive performance of VAEs and QVCs in modeling drug-disease gene networks within the Wnt signaling pathway in periodontal inflammation.