Quantum Machine Learning Framework for Virtual Screening in Drug Discovery: a Prospective Quantum Advantage
This work addresses the problem of faster and cost-effective drug discovery, especially for emerging diseases like COVID-19, by introducing a quantum-enhanced method that is incremental in its integration of quantum components.
The paper tackles virtual screening in drug discovery by proposing a quantum machine learning framework that combines classical support vector classifiers with quantum kernel estimation, showing that it can provide a tangible advantage over classical algorithms in some instances, with hardware simulations on IBM Quantum processors confirming predicted performances and surpassing classical equivalents.
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.