Quantum Chemistry in Drug Design: Applications, Challenges, and the Need for Speed.

Quantum mechanics (QM) provides a rigorous framework for modeling molecular interactions, making it increasingly valuable in drug design. However, its broader adoption is limited by computational cost and scalability. This manuscript reviews key QM applications-torsional profiles, spectra prediction, reactivity analysis, and modeling of non-covalent interactions-highlighting their impact and limitations. A detailed in-house study compares multiple methods across 3000 drug-like fragments, illustrating the trade-off between speed and accuracy. While machine learning and hybrid approaches offer faster alternatives, they often fall short of density functional theory (DFT) precision. The manuscript also explores scaling strategies and the emerging role of quantum computing. To fully leverage QM in drug discovery, faster and more scalable methods are essential.