In recent years, deep learning has significantly impacted drug discovery, enhancing molecular property prediction and virtual screening. Traditional models, such as convolutional neural networks and recurrent neural networks, excel in processing data organized in regular grids and sequences. However, scientific and real-world data often involve complex, non-Euclidean structures, such as point clouds, meshes, and various topological spaces. This talk will focus on Topological Deep Learning (TDL), a novel research area that extends deep learning to accommodate these complex data types. TDL integrates topological concepts to analyze higher-order relationships among entities, utilizing structures like simplicial complexes and hypergraphs. This approach allows for a more comprehensive representation of molecular interactions and hierarchies, capturing global dependencies and qualitative spatial properties. We will explore how TDL can significantly enhance drug discovery by improving the accuracy of predictive models and refining drug design strategies. By effectively addressing the complexities of molecular data, TDL has the potential to fundamentally advance therapeutic development and streamline the drug discovery process.