G　protein-coupled　receptors　(GPCRs)　have　been　the　targets　for　more　than　40%　of　the　currently　approved　drugs.　Although　neural　networks　can　effectively　improve　the　accuracy　of　prediction　with　the　biological　activity,　the　result　is　undesirable　in　the　limited　orphan　GPCRs　(oGPCRs)　datasets.　To　this　end,　we　proposed　Multi-source　Transfer　Learning　with　Graph　Neural　Network,　called　MSTL-GNN,　to　bridge　this　gap.　Firstly,　there　are　three　ideal　sources　of　data　for　transfer　learning,　oGPCRs,　experimentally　validated　GPCRs,　and　invalidated　GPCRs　similar　to　the　former　one.　Secondly,　the　SIMLEs　format　GPCRs　convert　to　graphics,　and　they　can　be　the　input　of　Graph　Neural　Network　(GNN)　and　ensemble　learning　for　improving　prediction　accuracy.　Finally,　our　experiments　show　that　MSTL-GNN　remarkably　improves　the　prediction　of　GPCRs　ligand　activity　value　compared　with　previous　studies.　On　average,　the　two　evaluation　indexes　we　adopted,　R2　and　Root-mean-square　deviation　(RMSE).　Compared　with　the　state-of-the-art　work　MSTL-GNN　increased　up　to　67.13%　and　17.22%,　respectively.　The　effectiveness　of　MSTL-GNN　in　the　field　of　GPCR　Drug　discovery　with　limited　data　also　paves　the　way　for　other　similar　application　scenarios.