Francesco Tacchino, Alessandro Chiesa, et al.
Journal of Materials Chemistry C
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.
Francesco Tacchino, Alessandro Chiesa, et al.
Journal of Materials Chemistry C
Arkopal Dutt, Edwin Pednault, et al.
PRResearch
Filippo Utro, Aritra Bose, et al.
ISMB 2024
Federico Balduini, Alan Molinari, et al.
CMD 2023