# Topological Deep Learning

TopoLIA extends attention from how much information is exchanged to how that information is transformed per target, beating its standard-attention counterpart on protein–ligand scoring and docking benchmarks and reaching an 88% docking success rate on CASF-2013.

TopoLIA extends attention from how much information is exchanged to how that information is transformed per target, beating its standard-attention counterpart on protein–ligand scoring and docking benchmarks and reaching an 88% docking success rate on CASF-2013.

Addressing the limitation that most topological deep learning approaches, being based on boundary operators and Hodge Laplacians, cannot lift or lower features and signals across more than one dimension per layer, this work proposes the adoption of cup and cap products and formulates the Cup and Cap Topological Neural Network (CCNN), a topological deep learning architecture designed to learn node-based variables (0-cochains) while accounting for many-body interactions such as triangles in the data, validated on the TopoBench datasets where it shows competitiveness with respect to other simplicial complex neural networks.

Addressing the limitation that most topological deep learning approaches, being based on boundary operators and Hodge Laplacians, cannot lift or lower features and signals across more than one dimension per layer, this work proposes the adoption of cup and cap products and formulates the Cup and Cap Topological Neural Network (CCNN), a topological deep learning architecture designed to learn node-based variables (0-cochains) while accounting for many-body interactions such as triangles in the data, validated on the TopoBench datasets where it shows competitiveness with respect to other simplicial complex neural networks.