Abstract: OBJECTIVE To construct new deep learning(DL) models for binding activity prediction against each of 23 low-data G-protein coupled receptors(GPCRs)(known binders <250) using MolMapNet, assisting in the novel drug discovery of GPCRs. METHODS Binding activity datasets of low-data GPCRs were collected from multiple databases and preprocessed, and DL models were constructed by MolMapNet; the established models were compared with published DL models and ML models; Neuropeptide S receptor proprietary compounds to evaluate the constructed model. RESULTS Under 10-fold cross-validation tests, MolMapNet DL models predicted the binding activity values of the test-set compounds for each GPCR with RMSE 0.373 6-1.199 8(20 among which RMSE<1), MAE 0.299 4-1.008 3(21 among which MAE<1), and R² 0.136 9-0.810 7(15 among which R² >0.5, 9 among which R² >0.6). Our low-sample models showed comparable performances to those of the published DL models trained with higher-data GPCRs(>250 known binders). Our models also performed well in activity prediction of patented GPCR binders. CONCLUSION The 23 models constructed here can predict the biological activity of a compound against a specific target with good performance, have the potential to screen drugs with novel structures, and MolMapNet architecture is useful for activity prediction against the low-sample GPCR targets.