PharmPD: Coupling Pharmacophore Semantics and 3D Geometry for Molecular Representation

Three-dimensional molecular representations capture spatial relationships, but geometry alone does not specify which of those relationships are chemically relevant to molecular recognition. We introduce PharmPD, an explicit representation that couples interaction semantics and three-dimensional geometry through chemistry-audited pharmacophore centers and their spatial relationships. Across 22 TDC ADMET benchmark tasks, adding PharmPD to a strong 2D baseline improves performance on 18 tasks and on all seven prespecified molecular-recognition endpoints. Controlled ablations show that pharmacophore semantics and geometric information are substantially more predictive when explicitly coupled than when provided separately, while three-dimensional distance provides additional value beyond topological separation. The same explicit structure also makes model attributions chemically inspectable: high-attribution features recover target-specific pharmacophore relationships consistent with established recognition chemistry and can be grounded in molecule-specific structural evidence, improving their verifiability. This complementary value persists on an external OpenADMET PXR task, including against established 3D descriptors and a strong baseline incorporating Uni-Mol v2 representations. Together, these results identify semantic–geometric conjunction as a useful design principle for molecular representations that improves prediction while preserving a direct, verifiable connection to molecular-recognition chemistry.