mathttQ2SAR: overcoming classical bottlenecks in drug discovery via quantum multiple kernel learning

Quantitative Structure-Activity Relationship (mathttQSAR) 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 (mathttQMKL) framework, dubbed Next-Gen mathttQ2SAR, that leverages Quantum Support Vector Machines (mathttQSVMs) 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 mathttDYRK1A kinase (a critical target for Alzheimer's disease), the mathttQMKL-mathttSVM achieves an impressive Area Under the Curve (mathttAUC) score of 0.8750, significantly outperforming classical state-of-the-art Gradient Boosting models (mathttAUC = 0.8037). Furthermore, we establish a theoretical and empirical pathway toward resolving classical data bottlenecks through projected quantum kernels (mathttPQK) 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.