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arXiv 2506.20587quant-phcond-mat.str-elphysics.bio-phphysics.chem-phphysics.comp-ph
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如何利用量子计算机计算生物分子自由能

How to use quantum computers for biomolecular free energies

University of Copenhagen(哥本哈根大学);ETH Zurich(苏黎世联邦理工学院);QuSoft, Centrum voor Wiskunde en Informatica(QuSoft,荷兰数学与计算机科学中心)
Jakob Günther, Thomas Weymuth, Moritz Bensberg, Freek Witteveen, Matthew S. Teynor, F. Emil Thomasen, Valentina Sora, William Bro-Jørgensen, Raphael T. Husistei… 展开作者

Jakob Günther, Thomas Weymuth, Moritz Bensberg, Freek Witteveen, Matthew S. Teynor, F. Emil Thomasen, Valentina Sora, William Bro-Jørgensen, Raphael T. Husistein, Mihael Erakovic, Marek Miller, Leah Weisburn, Minsik Cho, Marco Eckhoff, Aram W. Harrow, Anders Krogh, Troy Van Voorhis, Kresten Lindorff-Larsen, Gemma Solomon, Markus Reiher, Matthias Christandl

AI总结:

本文提出利用量子计算与机器学习结合的方法,高效计算生物分子自由能,通过量子嵌套策略提升准确性,适用于大分子体系的模拟。

摘要:

自由能计算是基于物理的生化过程分析的核心。它们允许我们量化分子识别机制,这些机制决定了从细胞信号传递到药物治疗疾病等多种生物现象。定量和预测性的自由能计算需要计算模型准确捕捉分子间的复杂电子相互作用以及分子及其水环境的熵贡献。然而,只有小原子模型才能获得准确的量子力学能量和力,而无法用于大生物大分子。本文演示了如何通过机器学习在集成算法中一致地将小亚结构的准确量子力学数据链接到生物分子复合物的总体势能。我们使用双重量子嵌套策略,其中最内层的量子核心以非常高的精度处理。我们通过传统量子化学方法演示了该方法在钌基抗癌药物与其蛋白质靶标分子识别中的可行性。由于这些方法随系统规模扩大而不利,我们分析了量子计算机提供高精度能量以影响最终自由能的需求。一旦满足这些需求,我们的计算管道FreeQuantum就能有效地利用量子计算的能量,从而实现量子计算增强的生化过程建模。这种方法结合了量子计算机对相互作用电子模拟的指数加速速度,与现代经典模拟技术相结合,利用机器学习来建模大分子。

英文摘要:

Free energy calculations are at the heart of physics-based analyses of biochemical processes. They allow us to quantify molecular recognition mechanisms, which determine a wide range of biological phenomena from how cells send and receive signals to how pharmaceutical compounds can be used to treat diseases. Quantitative and predictive free energy calculations require computational models that accurately capture both the varied and intricate electronic interactions between molecules as well as the entropic contributions from motions of these molecules and their aqueous environment. However, accurate quantum-mechanical energies and forces can only be obtained for small atomistic models, not for large biomacromolecules. Here, we demonstrate how to consistently link accurate quantum-mechanical data obtained for substructures to the overall potential energy of biomolecular complexes by machine learning in an integrated algorithm. We do so using a two-fold quantum embedding strategy where the innermost quantum cores are treated at a very high level of accuracy. We demonstrate the viability of this approach for the molecular recognition of a ruthenium-based anticancer drug by its protein target, applying traditional quantum chemical methods. As such methods scale unfavorable with system size, we analyze requirements for quantum computers to provide highly accurate energies that impact the resulting free energies. Once the requirements are met, our computational pipeline FreeQuantum is able to make efficient use of the quantum computed energies, thereby enabling quantum computing enhanced modeling of biochemical processes. This approach combines the exponential speedups of quantum computers for simulating interacting electrons with modern classical simulation techniques that incorporate machine learning to model large molecules.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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