Pallavi Gundala, Altalbe Ali, Kumar R Prasanna
Amrita School of Computing, Amrita Vishwa Vidyapeetham, Chennai, Tamil Nadu, India.
Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia.
Sci Rep. 2025 Jul 25;15(1):27103. doi: 10.1038/s41598-025-07303-z.
Drug-target interaction (DTI) prediction is a critical task in computational drug discovery, enabling drug repurposing, precise medicine, and large-scale virtual screening. Traditional in-silico methods, such as molecular docking, classical machine learning, and deep learning, have made significant progress in addressing this issue. However, existing approaches are hindered by computational inefficiencies, reliance on manual feature engineering, and struggles to generalize across diverse molecular structures, limiting their molecular capabilities. Recent advancements in Quantum Machine Learning (QML) are paving the way for its practical applications, unlocking unprecedented capabilities in predictive accuracy, scalability, and efficiency by leveraging the unique powers of quantum computing, namely superposition and entanglement. This study proposes QKDTI - Quantum Kernel Drug-Target Interaction, a novel quantum-enhanced framework for DTI prediction. It used Quantum Support Vector Regression (QSVR) with quantum feature mapping that takes into account a quantum feature space for molecular descriptors and allows encoding molecular and protein features, improved predictions of binding affinities. To enhance the model to be more computationally feasible, integration of the Nystrom approximation into the model allows providing an efficient kernel approximation while reducing overhead expenses. QKDTI was evaluated on benchmark datasets - Davis and KIBA, and validated independently on BindingDB. This model achieves 94.21% accuracy on DAVIS, 99.99% on KIBA, and 89.26% on BindingDB, significantly outperforming classical and other quantum models. Further, the statistical tests have been conducted on the compared models to provide the reliability of the results. This indicates that introducing quantum computing into DTI pipeline can revolutionize computational drug discovery by improving predictive accuracy and providing a better generalization over multiple datasets.
药物-靶点相互作用（DTI）预测是计算药物发现中的一项关键任务，有助于药物重新利用、精准医学和大规模虚拟筛选。传统的计算机模拟方法，如分子对接、经典机器学习和深度学习，在解决这个问题上取得了显著进展。然而，现有方法受到计算效率低下、依赖人工特征工程以及难以在不同分子结构中泛化的限制，从而限制了它们的分子能力。量子机器学习（QML）的最新进展为其实际应用铺平了道路，通过利用量子计算的独特能力，即叠加和纠缠，在预测准确性、可扩展性和效率方面解锁了前所未有的能力。本研究提出了QKDTI——量子核药物-靶点相互作用，这是一种用于DTI预测的新型量子增强框架。它使用了具有量子特征映射的量子支持向量回归（QSVR），该映射考虑了分子描述符的量子特征空间，并允许对分子和蛋白质特征进行编码，从而改进了结合亲和力的预测。为了使模型在计算上更可行，将Nystrom近似集成到模型中可以在减少开销的同时提供有效的核近似。QKDTI在基准数据集——Davis和KIBA上进行了评估，并在BindingDB上进行了独立验证。该模型在DAVIS上的准确率达到94.21%，在KIBA上达到99.99%，在BindingDB上达到89.26%，显著优于经典模型和其他量子模型。此外，还对比较模型进行了统计测试，以提供结果的可靠性。这表明将量子计算引入DTI流程可以通过提高预测准确性和在多个数据集上提供更好的泛化能力，彻底改变计算药物发现。
