The exploration of vastly enlarged chemical spaces, consisting of practical libraries with billions of achievable synthetic compounds, presents thrilling possibilities for drug development while also testing the limits of computational tools in selecting the top prospects. This particularly impacts quantum mechanics (QM) techniques, which offer highly precise chemical insights but are confined to smaller molecular scales. Central to numerous research initiatives is the balance between retaining precision and minimizing computational expense, as seen in advanced algorithms and novel computing methodologies. Developing customized QM-derived physics-based force fields, along with integrating QM approaches with machine learning—bolstered by high-performance computing infrastructure—will greatly expand the utility of these methods in drug research. The hurdle is significant, yet extraordinary breakthroughs are certain, marking the beginning of a groundbreaking period in the discipline.
