AI 中文总结
研究针对混合量子 - 经典蛋白质结构预测依赖惩罚权重问题，提出QFoldAgent多智能体框架，通过设计、VQE管道及反馈智能体协同优化。在两个数据集测试，该框架有效降低RMSD，提升结构有效性，减少失败情况。
AI 中文摘要
混合量子 - 经典蛋白质结构预测强烈依赖哈密顿惩罚权重，但现有的基于晶格的工作流程通常手动固定这些系数，且在模拟中仅评估非常短的片段。我们提出了QFoldAgent，这是一个用于5残基四面体晶格折叠的闭环多智能体框架。其中设计智能体提出序列条件惩罚，基于变分量子本征求解器（VQE）的量子 - 经典管道在Qiskit Aer噪声下优化所得哈密顿量，反馈智能体使用能量景观诊断和MolProbity验证信号在各循环中细化惩罚。真实度量如均方根偏差（RMSD）从不暴露给智能体，仅用于评估。我们在两个互补数据集上研究该框架：55个来自QDockBank且结构已知的片段以及100个覆盖优化的未见序列。在QDockBank基准测试中，QFoldAgent将中位RMSD从3.64 Å降至3.20 Å，在最难的目标上收益最大。在未见序列上，闭环将结构有效性从87.5%提高到98.7%，恢复了87%最初无效的情况，最强的控制器在87%的序列上改善了第3循环能量同时保持96%的拉氏构象偏好几何结构。这些结果表明，迭代智能体控制可系统地改善优化行为并减少5残基量子设置中的失败情况。
英文摘要
Hybrid quantum-classical protein structure prediction depends strongly on Hamiltonian penalty weights, yet existing lattice-based workflows typically fix these coefficients by hand and evaluate only very short fragments in simulation. We present QFoldAgent, a closed-loop multi-agent framework for 5-residue tetrahedral-lattice folding in which a design agent proposes sequence-conditioned penalties, a VQE-based quantum-classical pipeline optimizes the resulting Hamiltonian under Qiskit Aer noise, and a feedback agent uses energy-landscape diagnostics and MolProbity validation signals to refine penalties across cycles. Ground-truth metrics such as RMSD are never exposed to the agents and are used only for evaluation. We study the framework on two complementary datasets: 55 QDockBank-derived fragments with known structures and 100 coverage-optimized unseen sequences. On the QDockBank benchmark, QFoldAgent reduces median RMSD from 3.64 Å to 3.20 Å, with the largest gains on the hardest targets. On unseen sequences, the closed loop raises structural validity from 87.5% to 98.7%, recovers 87% of initially invalid cases, and the strongest controller improves cycle-3 energy on 87% of sequences while maintaining 96% Ramachandran-favored geometry. These results show that iterative agent control can systematically improve optimization behavior and reduce failure cases in a 5-residue quantum setting.
