Researchers are developing a quantum computing pipeline to aid in drug discovery, using it to study how drugs interact with their targets and the energy needed to break bonds within drugs.
Two case studies explored a prodrug of an anti-cancer drug and another drug targeting a specific genetic mutation, demonstrating the potential for real-world application of the pipeline.
Despite current limitations in accuracy, the research team hopes that this tool will democratize access to quantum computing in pharmaceutical research.
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Next Article Could help understand drug-target interactions and the energy required to break drug bonds
What is the story
Chinese scientists from Tencent Quantum Labs, China Pharmaceutical University, and AceMapAI Biotechnology have developed a quantum computing pipeline that could revolutionize drug design. The results of their research were published in the peer-reviewed journal Scientific Reports. The team believes that the superior computational power of quantum computing could revolutionize many scientific fields, including medicine. They also noted that current computational chemistry methods are not precise and become more expensive as the scale of calculations increases.
New approaches to drug design
The researchers created a hybrid quantum computing pipeline tailored for real-world drug discovery. The pipeline uses simulation and computation to address drug design challenges such as understanding how a drug interacts with a target and calculating the energy required to break bonds within a drug. The team validated the pipeline with two case studies dealing with real-world problems in drug design, demonstrating its potential for integration into real-world workflows.
A quantum computing pipeline applied to prodrug research
One of the tasks the team undertook was to determine the energy required to break the bonds in prodrugs. Prodrugs are important in modern pharmaceutical research because, according to the researchers, “they are only active in specific locations in the body, reducing the risk of side effects and leading to safer, more effective treatments.” They used their quantum computing pipeline to study an anti-cancer prodrug called beta-lapachone to see if it could react spontaneously in an organism.
Insights into anti-cancer drugs
In another case study, the researchers investigated an anti-cancer drug called sotorasib, which inhibits a specific genetic mutation called KRAS G12C. Using a hybrid quantum computing pipeline, they discovered that a strong covalent bond forms between the drug and the target mutation. This discovery provides valuable insight into the drug’s effectiveness against this specific genetic mutation. The team’s work illustrates how quantum computing can contribute to understanding and improving cancer treatment.
Tools for future drug discovery
The researchers believe that their quantum computing pipeline could become a foundational tool for drug discovery, even for those with no prior knowledge of quantum computing. They aim to democratize access to this advanced pipeline and lay the foundation for greater collaboration within the scientific community. This could accelerate the translation of quantum computing power into tangible therapeutic outcomes. However, they acknowledge that more work is needed to improve the accuracy of quantum computing methods due to current limitations.
