发表机构
Department of Computer Science; The Chinese University of Hong Kong; Department of Electrical and Computer Engineering; Rutgers University; The University of Chicago（计算机科学系; 香港中文大学; 电气与计算机工程系; 罗格斯大学; 芝加哥大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对分子对接这一药物发现瓶颈，引入Q分数，通过图神经网络编码能量，利用数字化反绝热量子近似优化算法解决最大权重顶点团问题评分，在多个蛋白质靶点及大量分子上取得良好效果，验证了在噪声中等规模量子硬件上的可解性。
AI 中文摘要
分子对接预测小分子与蛋白质的结合方式，是药物发现中的关键瓶颈。经典评分函数对经验性成对接触求和，忽略了如轨道电荷转移等决定结合特异性的量子力学效应。我们引入Q分数，将图神经网络预测的轨道供体-受体能量编码到加权图中，并通过数字化反绝热量子近似优化算法解决最大权重顶点团问题来对结合进行评分。每个相互作用锚点映射到一个量子比特，兼容性约束成为边。在11个蛋白质靶点上，数字化反绝热量子近似优化算法在10个量子比特时能在8个靶点上恢复精确最优解。在1000个人工智能生成的分子上，Q分数与经典评分正交，斯皮尔曼相关系数为0.05，由相关系数为0.90的轨道质量驱动，且无分子量偏差，以两倍于随机速率富集强轨道相互作用。数字化反绝热量子近似优化算法平均近似比为0.94，52%精确。在IBM Eagle上执行1000个电路证实了在噪声中等规模量子硬件上6量子比特的可解性。
英文摘要
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.
