论文标题
在药物发现中进行虚拟筛查的量子机学习框架：前瞻性量子优势
Quantum Machine Learning Framework for Virtual Screening in Drug Discovery: a Prospective Quantum Advantage
论文作者
论文摘要
基于配体的虚拟筛查（LB-VS）的机器学习（ML）是以更快，更具成本效益的方式发现新药的重要内部工具，尤其是对于诸如Covid-19之类的新兴疾病。在本文中，我们提出了一个通用框架，该框架结合了经典的支持向量分类器（SVC）算法与现实数据库中LB-VS的量子内核估计，我们主张支持其前瞻性量子优势。确实，我们从启发性地证明，至少在某些相关实例中，与在同一数据集上运行的最新经典算法相比，我们的量子集成工作流可以提供明显的优势，从而显示出对目标和特征选择方法的强烈依赖。最后，我们使用ADRB2和COVID-19数据集测试了IBM量子处理器上的算法，这表明硬件模拟提供了与预测的性能一致的结果，并且可以超越经典的等效物。
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.
