Liliopoulos Ioannis, Varsamis Georgios D, Karamanidou Theodora, Papalitsas Christos, Koulouras Grigorios, Pantazopoulos Vassilios, Stavropoulos Thanos G, Karafyllidis Ioannis G
Department of Electrical and Computer Engineering, Democritus University of Thrace, Xanthi, 67100, Greece.
Pfizer Center for Digital Innovation, Thessaloniki, Greece.
J Comput Aided Mol Des. 2025 Jul 5;39(1):40. doi: 10.1007/s10822-025-00620-5.
Over the past two decades, the development of novel drugs evolved into a high-demanding computational field. There is a constant and increasing need for advanced methods for determining protein-ligand binding in the drug design process. Even after the introduction and use of High-Performance Computers in drug design, fundamental problems and constraints have not been dealt with in a satisfactory manner. This is partially due to the fact that ligand docking in proteins is a quantum mechanical process. With the quantum computers available today, the question "Can quantum computers be used in drug design and how?" arises naturally. A novel quantum algorithm for protein-ligand docking site identification is presented here. In detail, the protein lattice model has been expanded to include protein-ligand interactions. Quantum state labelling for the interaction sites is introduced, and an extended and modified Grover quantum search algorithm is implemented to search for docking sites. This algorithm has been tested and executed on both a quantum simulator and a real quantum computer. The results show that the quantum algorithm can identify effectively docking sites. The quantum algorithm is highly scalable and well-suited for identifying docking sites within large proteins, poised to harness the potential of increased quantum bits in the future.
在过去二十年中，新型药物的研发已发展成为一个对计算要求很高的领域。在药物设计过程中，对于确定蛋白质 - 配体结合的先进方法的需求持续且不断增加。即使在药物设计中引入并使用了高性能计算机之后，一些基本问题和限制仍未得到令人满意的解决。部分原因在于蛋白质中的配体对接是一个量子力学过程。鉴于当今可用的量子计算机，“量子计算机能否用于药物设计以及如何使用？”这个问题自然而然地出现了。本文提出了一种用于蛋白质 - 配体对接位点识别的新型量子算法。具体而言，蛋白质晶格模型已得到扩展，以纳入蛋白质 - 配体相互作用。引入了相互作用位点的量子态标记，并实现了一种扩展和改进的格罗弗量子搜索算法来搜索对接位点。该算法已在量子模拟器和真实量子计算机上进行了测试和执行。结果表明，该量子算法能够有效地识别对接位点。该量子算法具有高度的可扩展性，非常适合识别大型蛋白质中的对接位点，有望在未来利用增加量子比特的潜力。
