Message　passing　neural　network　(MPNN)　is　one　of　the　excellent　deep　learning　models　for　drug　discovery　and　development,　drug　laboratory　usually　outsource　the　MPNN　model　to　cloud　servers　to　save　the　research　and　development　cost.　However,　drug-related　data　privacy　has　become　a　noticeable　hindrance　for　outsourcing　cooperation　in　drug　discovery.　In　this　paper,　we　propose　a　lightweight　privacy-preserving　message　passing　neural　network　framework　(SecMPNN)　for　property　prediction　in　new　drugs.　To　implement　SecMPNN,　we　design　multiple　protocols　to　perform　the　three　stages　of　MPNN,　namely　message　function,　update　function,　and　readout　function.　The　above　new-designed　secure　protocols　enable　SecMPNN　to　adapt　to　the　different　numbers　of　participating　servers　and　different　lengths　of　encryption　requirements.　Moreover,　the　accuracy,　efficiency,　and　security　of　SecMPNN　are　demonstrated　through　comprehensive　theoretical　analysis　and　a　large　number　of　experiments.　The　experimental　results　show　the　communication　efficiency　in　multiplication　and　comparison　increases　27.78%　and　58.75%,　the　computation　error　decreases　to　4.64%.　©　2021,　ICST　Institute　for　Computer　Sciences,　Social　Informatics　and　Telecommunications　Engineering.