The team wrote: “Quantum computing, with its high computational efficiency compared to traditional methods, has the potential to revolutionize many areas of science, including medicine.
Existing traditional methods of computational chemistry are imprecise – and their costs rise as the scale of computing grows, the researchers said.
“However, in the current environment, the contribution of quantum computing to drug discovery is limited mainly to the validation of concepts, with little integration into the actual design of drugs,” the team said.
In response, the team developed a hybrid quantum computing pipeline aimed at real-world drug discovery, which they were able to validate using two case studies dealing with real-world problems in drug design. .
“Our results demonstrate the potential of the quantum computing pipeline for integration into a real-world drug design process,” the researchers said.
The group sought to perform two important tasks in drug discovery: to determine the energy required to close or break the bonds in the prodrug – the drug that goes from inactive to active in the body – and simulation of covalent bonds, a chemical compound where atoms. share electrons.
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Another strategy for putting these drugs into action is to break the carbon-carbon bonds. According to the group, the amount of energy interference for the breaking of these bonds is “important”, since it determines whether it can happen intentionally inside the body.
They compared their computer results with a 2022 paper that used traditional computer methods to determine the energy barrier as well as laboratory experiments.
The analysis using a quantum computer agreed with the previous study, with both analyzes showing that the drug can have a specific response in living organisms.
“Our results demonstrate the efficiency of quantum computing … as well as the flexibility and plug-and-play advantages of our pipeline,” the researchers wrote.
In their second study, the team sought to find out how another anti-cancer drug, sotorasib, known as a KRAS (Kirsten Rat Sarcoma) inhibitor) works, which blocks certain gene mutations in KRAS, G12C.
Finding drugs for genetic mutations of this oncogene has been a challenge, as it needs to form covalent bonds with the target in order to prevent it.
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Quantum mechanics and molecular mechanics – important simulations in post-design drug validation – were used to evaluate drug-target interactions. The team used a hybrid computing approach, meaning they started with a quantum emulator before moving to a quantum computer.
After performing hybrid quantum computer validation on sotorasib and its target mutation, the team realized that there are strong covalent bonds between them – which could provide insight into the drug’s effectiveness.
“This understanding is important for the precise design of future inhibitors that target the same mutations,” said the researchers, adding that it will ensure future development of speed and precision. in drug discovery using quantum computing.
“In this research, we have created a model pipeline that enables quantum computers to tackle real-world drug discovery problems,” they said.
“The versatility of our pipeline highlights its potential as a basic tool, empowering researchers with ready-to-use tools.”
They said even drug designers with no knowledge of quantum computing would be able to use them.
They also say more work is needed to improve the accuracy of quantum computing methods for drug discovery. Another problem is the current limitations of quantum computers, such as long computational time and errors.
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