Integrating equivariant architectures and charge supervision for data-efficient molecular property prediction

Molecular equivariant transformer (MET) learns symmetry-aware, charge-guided representations that capture geometric and electronic structure, enabling data-efficient and interpretable molecular property prediction across diverse tasks.