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Abstract
Quantitative Structure-Activity Relationship (\texttt{QSAR}) modeling is key in drug discovery, but classical methods face limitations when handling high-dimensional data and capturing complex molecular interactions. This research proposes enhancing \texttt{QSAR} techniques through Quantum Support Vector Machines (\texttt{QSVMs}), which leverage quantum computing principles to process information Hilbert spaces. By using quantum data encoding and quantum kernel functions, we aim to develop more accurate and efficient predictive models.
