Quantum machine learning framework for virtual screening in drug discovery: a prospective quantum advantage. Issue 1 (17th February 2023)

• Record Type: Journal Article
• Authors: Mensa, Stefano; Sahin, Emre; Tacchino, Francesco; Kl Barkoutsos, Panagiotis; Tavernelli, Ivano
• Is Part Of: Machine learning: science and technology. Volume 4:Issue 1(2023)
• Publication Date: 2023-02-17
• Subjects: quantum machine learning -- quantum kernel methods -- machine learning -- support vector classifier -- drug discovery -- ligand based -- virtual screening; 006.31
• DOI: 10.1088/2632-2153/acb900
• Languages: English
• ISSNs: 2632-2153

Abstract

Abstract: Machine Learning 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 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.