mathtt{Q^2SAR}: overcoming classical bottlenecks in drug discovery via quantum multiple kernel learning

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.