Quantum Machine Learning Framework for Virtual Screening in Drug Discovery: a Prospective Quantum Advantage. (arXiv:2204.04017v1 [quant-ph])

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 …

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