QFoldAgent: An Autonomous Quantum Optimization Multi-Agent System for Protein Structure Prediction
QFoldAgent: An Autonomous Quantum Optimization Multi-Agent System for Protein Structure Prediction
QFoldAgent：用于蛋白质结构预测的自主量子优化多智能体系统
Abstract: 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.
摘要： 混合量子-经典蛋白质结构预测在很大程度上依赖于哈密顿惩罚权重（Hamiltonian penalty weights），然而现有的基于晶格的工作流程通常手动固定这些系数，并且仅在模拟中评估极短的片段。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。在该框架中，设计智能体提出序列条件惩罚，基于 VQE（变分量子本征求解器）的量子-经典流水线在 Qiskit Aer 噪声下优化生成的哈密顿量，反馈智能体则利用能量景观诊断和 MolProbity 验证信号在循环中优化惩罚参数。
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.
RMSD 等基准指标从未向智能体公开，仅用于评估。我们在两个互补的数据集上研究了该框架：55 个具有已知结构的 QDockBank 衍生片段和 100 个覆盖率优化的未知序列。在 QDockBank 基准测试中，QFoldAgent 将中位 RMSD 从 3.64 Å 降低至 3.20 Å，在最困难的目标上取得了最大的提升。
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.
在未知序列上，该闭环系统将结构有效性从 87.5% 提高到 98.7%，恢复了 87% 的初始无效案例；最强的控制器在 87% 的序列上改善了第三轮循环的能量，同时保持了 96% 的拉氏图（Ramachandran）偏好几何结构。这些结果表明，迭代智能体控制可以系统地改善 5 残基量子环境下的优化行为并减少失败案例。