CASE STUDY

Developing new drugs

Conventional computers can only approximate the behaviour of complex molecules, because accurately modelling the quantum mechanics of atoms and electrons requires enormous computing power. As molecules become larger, the number of possible interactions grows rapidly, making precise simulations impractical even for today's most powerful supercomputers.

Quantum computers are designed to work with problems that follow the rules of quantum physics. This makes them particularly well suited to simulating molecular systems and chemical reactions. Rather than relying on simplified models, they could provide a more realistic picture of how a drug binds to a target protein, how stable a molecule is, or how small changes in its structure affect its performance.

Although current quantum computers are still developing and cannot yet replace conventional computing, hybrid approaches that combine classical and quantum methods are already being explored for some stages of pharmaceutical research.

Pharmaceutical companies, technology providers and research organisations are investigating how quantum computing can accelerate different parts of the drug discovery process. One promising application is molecular simulation, where quantum computers could improve predictions of how drug candidates interact with disease targets before laboratory testing begins.

Quantum computing may also support virtual screening, enabling researchers to evaluate large numbers of potential compounds more efficiently and identify the most promising candidates for further development. Other potential applications include optimising chemical synthesis, improving machine learning models used in drug design and supporting personalised medicine by analysing complex biological data.

In the near term, quantum computing is expected to complement, rather than replace, existing computational tools. Researchers will continue to combine artificial intelligence, high-performance computing and laboratory experiments with quantum algorithms to improve efficiency while reducing the time and cost of developing new medicines.

Healthcare systems face increasing pressure to deliver new medicines more quickly, reduce development costs and improve patient outcomes. Many diseases remain difficult to treat because existing methods cannot fully model the complexity of biological systems. Quantum computing offers the potential to improve understanding of molecular behaviour, helping researchers identify promising treatments sooner and reduce the number of unsuccessful drug candidates entering costly clinical trials. While the technology is still maturing, continued advances could make drug development faster, more efficient and more targeted over the coming decade.

• Designing more effective cancer treatments through improved molecular modelling.
• Accelerating the discovery of new antibiotics to address antimicrobial resistance.
• Developing medicines for rare diseases where research resources are limited.
• Improving personalised medicine by matching treatments to an individual's biology.
• Predicting drug safety and side effects earlier in development.
• Optimising manufacturing processes for complex pharmaceutical compounds.