  Machine Learning (ML) for Ligand Based Virtual Screening (LB-VS) is an
important in-silico tool for discovering new drugs in a faster and
cost-effective manner, especially for emerging diseases such as COVID-19. In
this paper, we propose a general-purpose framework combining a classical
Support Vector Classifier (SVC) algorithm with quantum kernel estimation for
LB-VS on real-world databases, and we argue in favor of its prospective quantum
advantage. Indeed, we heuristically prove that our quantum integrated workflow
can, at least in some relevant instances, provide a tangible advantage compared
to state-of-art classical algorithms operating on the same datasets, showing
strong dependence on target and features selection method. Finally, we test our
algorithm on IBM Quantum processors using ADRB2 and COVID-19 datasets, showing
that hardware simulations provide results in line with the predicted
performances and can surpass classical equivalents.
