发表机构
Technologies Delaware, USA（德克萨斯州技术公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对经典QSAR方法在药物发现中映射分子数据复杂关系的局限，提出$\mathtt{Q^2SAR}$框架，利用量子多核学习及支持向量机增强非线性建模表现力，在$\mathtt{DYRK1A}$激酶数据集上表现优异，为药物发现开辟新途径。
AI 中文摘要
定量构效关系（$\mathtt{QSAR}$）建模是早期药物发现中的一种基础计算方法，常用于预测化合物毒性、生物利用度和治疗潜力。然而，经典方法难以有效映射分子数据中固有的高度复杂、非线性和高维相互作用，导致预测准确性降低和后期临床失败成本高昂。本文提出了一种量子多核学习（$\mathtt{QMKL}$）框架，即下一代$\mathtt{Q^2SAR}$，它利用量子支持向量机（$\mathtt{QSVMs}$）克服这些经典限制。通过将分子描述符编码到指数级大的量子希尔伯特空间中，该方法显著提高了非线性建模的表现力。在针对$\mathtt{DYRK1A}$激酶（阿尔茨海默病的关键靶点）的数据集上对量子增强框架进行基准测试，$\mathtt{QMKL}$-$\mathtt{SVM}$实现了令人印象深刻的曲线下面积（$\mathtt{AUC}$）得分0.8750，显著优于经典的梯度提升模型（$\mathtt{AUC}=0.8037$）。此外，我们通过投影量子核（$\mathtt{PQK}$）和测量加速器建立了一条解决经典数据瓶颈的理论和经验途径。随着量子计算架构的成熟，该框架为自主认知架构和自我改进的药物发现管道铺平了道路，有望在广阔的化学空间中获得更深入的见解，并加速救命疗法的开发。
英文摘要
Quantitative Structure-Activity Relationship ($\mathtt{QSAR}$) modeling is a foundational computational methodology in early-stage drug discovery, heavily relied upon for predicting compound toxicity, bioavailability, and therapeutic potential. However, classical methods often struggle to effectively map the highly complex, non-linear, and high-dimensional interactions inherent in molecular data, leading to reduced predictive accuracy and costly late-stage clinical failures. In this paper, we present a Quantum Multiple Kernel Learning ($\mathtt{QMKL}$) framework, dubbed Next-Gen $\mathtt{Q^2SAR}$, that leverages Quantum Support Vector Machines ($\mathtt{QSVMs}$) to overcome these classical limitations. By encoding molecular descriptors into exponentially large quantum Hilbert spaces, our approach substantially enhances the expressiveness of non-linear modeling. Benchmarking our quantum-enhanced framework on a dataset targeting the $\mathtt{DYRK1A}$ kinase (a critical target for Alzheimer's disease), the $\mathtt{QMKL}$-$\mathtt{SVM}$ achieves an impressive Area Under the Curve ($\mathtt{AUC}$) score of $0.8750$, significantly outperforming classical state-of-the-art Gradient Boosting models ($\mathtt{AUC} = 0.8037$). Furthermore, we establish a theoretical and empirical pathway toward resolving classical data bottlenecks through projected quantum kernels ($\mathtt{PQK}$) and measurement accelerators. As quantum computing architecture matures, this framework paves the way for autonomous cognitive architectures and self-improving drug discovery pipelines, promising to unlock deeper insights across vast chemical spaces and to accelerate the development of life-saving therapeutics.
