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This paper proposes ChemHyperMag, a physics-informed magnetic hypergraph learning method for multitask ADMET prediction that uses functional group hypergraphs and a Hermitian magnetic Laplacian to capture asymmetric interactions and directional signals, improving prediction accuracy with fewer labeled samples.