Cleveland Clinic and Kent State Team Use Quantum Computing to Advance Drug Design
Researchers at the Cleveland Clinic, in collaboration with PhD students from Kent State University, have developed a quantum computing-based method that significantly improves drug design, outperforming traditional computational approaches.
The project is a key step in drug development. Proteins fold into complex three-dimensional structures that determine how they function and bind to molecules.
Identifying the most stable, low-energy configuration is essential but computationally challenging, even for the fastest supercomputers.
The team employed the Variational Quantum Eigensolver (VQE), a hybrid quantum-classical approach. IBM Quantum System One generated possible protein shapes, while classical computers filtered for the lowest-energy structures.
Quantum computing then refined the final fragments by adding missing atoms and charges, enabling accurate predictions of drug binding.
This method successfully predicted the structures of 23 protein fragments and seven real-world drug targets, surpassing current classical methods, including AlphaFold3, in both accuracy and binding prediction.
