TL;DR
This chapter outlines key lessons from integrating FMO-based QM workflows into high-throughput pipelines, focusing on challenges such as input preparation, charge assignment, minimization, and truncation, error classification, and output parsing.
Key QM applications-torsional profiles, spectra prediction, reactivity analysis, and modeling of non-covalent interactions-highlighting their impact and limitations are reviewed, illustrating the trade-off between speed and accuracy.
This chapter presents the perspective of computational chemists at Sygnature Discovery on the growing need to integrate quantum mechanics (QM) with artificial intelligence (AI) to improve the efficiency and effectiveness of modern drug design.
Recent developments in QM-based molecular docking and HTD are reviewed, focusing on cases where QM is used explicitly in docking workflows.
A theoretical review of how quantum computation integrates throughout the drug discovery pipeline, from target identification to lead optimisation, and critically distinguishes near-term NISQ capabilities from fault-tolerant quantum computing (FTQC) requirements.
This Perspective surveys the central methodological challenges in developing ML/MM frameworks, including the generation of high-quality reference data and the treatment of multiscale coupling.
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