{"ID":6537417,"CreatedAt":"2026-07-14T02:54:43.516908796Z","UpdatedAt":"2026-07-15T03:28:55.185153975Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.11701","arxiv_id":"2607.11701","title":"$\\mathtt{Q^2SAR}$: overcoming classical bottlenecks in drug discovery via quantum multiple kernel learning","abstract":"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.","short_abstract":"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 com...","url_abs":"https://arxiv.org/abs/2607.11701","url_pdf":"https://arxiv.org/pdf/2607.11701v1","authors":"[\"Mariano Caruso\",\"Daniel Ruiz\",\"Alejandro Giraldo\",\"Guido Bellomo\"]","published":"2026-07-13T15:33:05Z","proceeding":"quant-ph","tasks":"[\"quant-ph\",\"cs.LG\"]","methods":"[]","has_code":false}