用量子算法计算分子对接中的轨道相互作用，突破传统方法局限。
Q-Score: A Quantum-Native Scoring Function for Molecular Docking
- 将分子轨道能量建模为加权图，通过量子优化求解最佳结合构型。
- 在11个靶点中8个达到最优解，对1000个生成分子的预测与经典方法几乎无关。
- 适合追求高精度结合预测的药物研发者，尤其关注轨道效应的场景。
分子对接用于预测小分子与蛋白质的结合方式，是药物发现的关键瓶颈。传统打分函数依赖经验成对接触，忽略轨道电荷转移等量子效应。本文提出Q-Score，将GNN预测的轨道供体-受体能量编码为加权图，通过数字化反绝热量子近似优化算法（DC-QAOA）求解最大权重顶点团问题。每个相互作用锚点对应一个量子比特，兼容性约束变为边。在11个蛋白靶点上，60%以上在10量子比特下恢复精确最优解；在1000个AI生成分子上，Q-Score与经典打分函数相关性极低（斯皮尔曼ρ=0.05），但与轨道质量高度相关（ρ=0.90），且无分子量偏差，强轨道相互作用富集率是随机的两倍。在IBM Eagle硬件上执行1000个电路，证实6量子比特可在当前噪声量子设备（NISQ）上求解。
原文摘要 · Abstract (English)
Molecular docking predicts how a small molecule binds to a protein and is a key bottleneck in drug discovery. Classical scoring functions sum empirical pairwise contacts, blind to quantum-mechanical effects like orbital charge transfer that govern binding specificity. We introduce Q-Score, encoding GNN-predicted orbital donor-acceptor energies into a weighted graph and scoring binding by solving a maximum-weight vertex clique problem via Digitized-Counterdiabatic QAOA. Each interaction anchor maps to one qubit and compatibility constraints become edges. Across 11 protein targets, DC-QAOA recovers the exact optimum on 8 at 10 qubits. On 1000 AI-generated molecules, Q-Score is orthogonal to classical scoring with Spearman rho of 0.05, driven by orbital quality with rho of 0.90, and free of molecular-weight bias, enriching for strong orbital interactions at twice the random rate. DC-QAOA achieves a mean approximation ratio of 0.94 with 52 percent exact. Execution of 1000 circuits on IBM Eagle confirms 6-qubit solvability on NISQ hardware.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。