Research News

Cleveland Clinic Genome Center director Feixiong Cheng, PhD, and Kent State University scientist Qiang Guan, PhD, led a team of Kent State University PhD students in exploring how quantum computing can revolutionize drug design. 

Current design and research methods can take years to develop one drug. One of the main challenges of drug design is the complex calculations required to predict how minute changes in molecules and proteins will affect a drug’s effectiveness. These calculations are so complex that even the most powerful supercomputers have difficulty completing them.  

In a study published in Advanced Science, Dr. Cheng and his team demonstrated how quantum computing can solve these complex problems faster and more accurately than current methods.  

Quantum computing training for breakthrough discovery  

Quantum computing is a new method of computing that is faster and more powerful than even the most advanced supercomputer. To take advantage of its computational power for drug design, Dr. Cheng and his team decided to focus on protein folding. 

A protein folds itself into a structure that then determines how it functions and binds to other molecules in the body. There are countless possibilities for how a protein will fold and how this will affect its ability to bind with molecules. The lowest energy shape of a protein is the most stable and the most important to drug design.  

Dr. Cheng met the PhD students, including first author Yuqi Zhang, through the joint Cleveland Clinic and Kent State University PhD program. 

“I met and worked with these students about three years ago on computational drug discovery,” Dr. Cheng says. “We had this idea, but quantum computing was very new to us, so we had to train before pursuing the project.”  

Dr. Cheng encouraged the Kent State students to train on quantum computing and put their new skills to the test while helping to design the method for this study. 

The team decided to use a method called Variational Quantum Eigensolver (VQE) to find the lowest energy protein fragment. VQE is a hybrid method that combines the power of a quantum computer with the accuracy of a classical computer. First, the researchers used IBM Quantum System One on Cleveland Clinic’s main campus to predict all the possible shapes of the protein. Then, investigators used a classical computer to cut the results down to shapes with low energy. This process is repeated until only the lowest energy fragments are left.  

The final step of the process is for the quantum computer to refine the final shape by adding missing atoms and charges. This helps researchers test the drug to see if it binds to the correct molecules.  

This method was able to predict the structures of 23 protein fragments and seven real-world drug targets. For these tests, it was also able to outperform current classical computing methods, including AlphaFold3, in accuracy and drug binding. 

Developing quantum computing’s potential  

Though quantum computing holds the potential to solve some of healthcare’s toughest challenges, this computing method is still in its early stages. Researchers are laying the groundwork for breakthrough research by developing new algorithms and methods that can help quantum computing reach its full potential.  

“Our goal with this study was not just to achieve a more accurate method for protein structure prediction,” Dr. Cheng says. “This project can also help predict drug binding affinities that have the potential to improve the drug design process and help us create better drugs, faster.” 

Learn how Cleveland Clinic’s researchers are paving the way for quantum computing in healthcare and life sciences research.