WMAGT , an aggregated model of a graph convolutional network and graph transformer, it aims to find relation between diseases and drugs; discovering new indications for drugs and understanding drugs safety-profiles. The model was created by integrating three networks; drugdrug similarity, disease-disease similarity, and drug-disease association networks, followed by an end-to-end model to find unknown patterns and associations. WMAGT outperformed five state-of-the-art methods. To test the applicability of WMAGT, it was challenged to configure drugs for Parkinson's disease; seven predicted drugs were relevant to Parkinson's as shown in further literature analysis.