How Can QM Methods Be Used to Guide Synthesis Planning in Drug Discovery?

Quantum mechanical (QM) methods are traditionally used in drug discovery to support molecular design and property prediction; however, they are becoming increasingly important for evaluating synthetic feasibility during the Design-Make-Test-Analyze (DMTA) cycle. As modern tools such as virtual screening and machine-learning workflows rapidly propose many potential candidate molecules, their progression ultimately depends on whether they can be prepared efficiently in the laboratory. QM approaches provide a mechanistically grounded framework for evaluating thermodynamic and kinetic feasibility, predicting selectivity, and identifying competing reaction pathways before experimental effort is invested. This chapter reviews how QM methods can guide synthesis planning by complementing chemist expertise, literature precedent, and computer-assisted synthesis planning (CASP) tools. The application of density functional theory (DFT) to evaluate reaction energetics, transition states, and stereochemical outcomes is discussed, as well as the role of QM-derived reactivity descriptors to enable the quantitative ranking of retrosynthetic disconnections. An automated reaction-path exploration method is also highlighted for analyzing complex pericyclic and stereochemically rich transformations. These approaches demonstrate how QM-driven synthesis planning can support the identification of synthetically accessible drug candidates and advance progress in making the compounds, which is often the rate-limiting step in drug discovery. Some shortcomings of QM methods are also acknowledged, particularly the difficulty of fully modeling complex chemical reactions, which consequently restricts the breadth of their practical applications.