  End-to-end topological learning using 1-parameter persistence is well-known.
We show that the framework can be enhanced using 2-parameter persistence by
adopting a recently introduced 2-parameter persistence based vectorization
technique called GRIL. We establish a theoretical foundation of differentiating
GRIL producing D-GRIL. We show that D-GRIL can be used to learn a bifiltration
function on standard benchmark graph datasets. Further, we exhibit that this
framework can be applied in the context of bio-activity prediction in drug
discovery.
