QFoldAgent: An Autonomous Quantum Optimization Multi-Agent System for Protein Structure Prediction
QFoldAgent is a closed-loop multi-agent framework for 5-residue tetrahedral-lattice folding that uses a design agent, a VQE-based quantum-classical pipeline, and a feedback agent to iteratively refine Hamiltonian penalties. It reduces median RMSD from 3.64 Å to 3.20 Å on QDockBank fragments and raises structural validity from 87.5% to 98.7% on unseen sequences.
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[Submitted on 11 May 2026]
Title:QFoldAgent: An Autonomous Quantum Optimization Multi-Agent System for Protein Structure Prediction
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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. 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.
Subjects:
Artificial Intelligence (cs.AI)
Cite as: arXiv:2607.22549 [cs.AI]
(or arXiv:2607.22549v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2607.22549
arXiv-issued DOI via DataCite
Submission history
From: Winson Chen [view email] [v1] Mon, 11 May 2026 20:40:54 UTC (1,275 KB)
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