Feilong Wang

Capturing Motif Topological Diversity via Geometry-Adaptive Riemannian Molecular Representation Learning

Molecular properties are often governed by a small number of local substructures, or motifs, whose topologies can vary drastically across molecules. Existing molecular representation learning approaches typically embed all motifs into a single Euclidean or fixed-curvature space, which fails to capture the motif-level t...