Quantum Machine Learning Framework for Virtual Screening in Drug Discovery: a Prospective Quantum Advantage

• URL: http://arxiv.org/abs/2204.04017v1
• Date: Fri, 8 Apr 2022 12:05:27 GMT
• Title: Quantum Machine Learning Framework for Virtual Screening in Drug Discovery: a Prospective Quantum Advantage
• Authors: Stefano Mensa, Emre Sahin, Francesco Tacchino, Panagiotis Kl. Barkoutsos and Ivano Tavernelli
• Abstract summary: We show that a quantum integrated workflow can provide a tangible advantage compared to state-of-art classical algorithms. We also 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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