This research activity focuses on developing a Chemical Graph Network that captures the commonalities in chemical compounds. The network aims to provide a comprehensive understanding of the relationships between different chemical entities, enabling the identification of patterns and trends that can inform the development of new compounds and materials. By leveraging the power of graph theory and machine learning algorithms, this project seeks to create a robust and scalable platform for analyzing and visualizing chemical data, ultimately contributing to the advancement of chemical research and innovation.