QuTIE：面向酶靶标识别的量子优化方法
QuTIE: Quantum optimization for Target Identification by Enzymes
AI总结：
本文提出QuTIE方法，将酶靶标识别这一NP完全问题转化为QUBO形式并在量子计算机上求解，在27个代谢网络上取得最优或接近最优结果，并验证了14类疾病的已知酶靶标。
摘要：
酶靶标识别（Target Identification by Enzymes，TIE）问题旨在给定代谢网络中识别一组酶，使得抑制这些酶能够消除与疾病相关的一组目标化合物，同时对其他化合物造成的损伤最小。这是一个NP完全问题，因此基于经典计算机的最优解方法难以扩展到大规模代谢网络。本文研究代谢网络中用于药物靶标识别的TIE问题。我们提出了首个针对该NP完全问题的量子优化解决方案，称为QuTIE（Quantum optimization for Target Identification by Enzymes）。具体做法是：首先建立TIE问题的等价二次无约束二值优化（Quadratic Unconstrained Binary Optimization，QUBO）形式，然后将其映射为逻辑图，再将逻辑图嵌入到量子计算机上的硬件图中。我们在来自大肠杆菌（Escherichia coli）、智人（Homo sapiens）和小家鼠（Mus musculus）的27个代谢网络上进行了实验，结果表明QuTIE能够给出最优或接近最优的解。实验还证明，QuTIE能够成功识别出已在湿实验中得到验证的、覆盖14种主要疾病类别的酶靶标。
英文摘要：
Target Identification by Enzymes (TIE) problem aims to identify the set of enzymes in a given metabolic network, such that their inhibition eliminates a given set of target compounds associated with a disease while incurring minimum damage to the rest of the compounds. This is an NP-complete problem, and thus optimal solutions using classical computers fail to scale to large metabolic networks. In this paper, we consider the TIE problem for identifying drug targets in metabolic networks. We develop the first quantum optimization solution, called QuTIE (Quantum optimization for Target Identification by Enzymes), to this NP-complete problem. We do that by developing an equivalent formulation of the TIE problem in Quadratic Unconstrained Binary Optimization (QUBO) form, then mapping it to a logical graph, which is then embedded on a hardware graph on a quantum computer. Our experimental results on 27 metabolic networks from Escherichia coli, Homo sapiens, and Mus musculus show that QuTIE yields solutions which are optimal or almost optimal. Our experiments also demonstrate that QuTIE can successfully identify enzyme targets already verified in wet-lab experiments for 14 major disease classes.
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。
