Hyper-Dimensional Fingerprints as Molecular Representations

ArXi:2604.27810v1 Announce Type: new Computational molecular representations underpin virtual screening, property prediction, and materials discovery. Conventional fingerprints are efficient and deterministic but lose structural information through hash-based compression, particularly at low dimensionalities. Learned representations from graph neural networks recover this expressiveness but require task-specific