For FC-individual, we train each testing assay separately with a two-layer fully-connected base learner. For FC-All, a two-layer fully connected model is trained on samples from both support setandquery setofsource assays andfrom thesupport setofthetargetassay. C.1 DrugActivityPredictionData For drug activity prediction, here we summarized the number of assays belonging to each target family: GPCR (685), Ion channel (215), Kinase (665), NHR (123), Binding (2523), Phenotypic (2299), Functional (1689), Proteinase (289),ADME (55).

For FC-individual, we train each testing assay separately with a two-layer fully-connected base learner. For FC-All, a two-layer fully connected model is trained on samples from both support setandquery setofsource assays andfrom thesupport setofthetargetassay. C.1 DrugActivityPredictionData For drug activity prediction, here we summarized the number of assays belonging to each target family: GPCR (685), Ion channel (215), Kinase (665), NHR (123), Binding (2523), Phenotypic (2299), Functional (1689), Proteinase (289),ADME (55).