Quantum computing can solve complex problems, simulating interactions between molecules and predicting the success and safety of drugs, which can aid in drug design.
“Quantum computing offers superior computational power compared to classical approaches and has the potential to revolutionize many areas of science, including medicine,” the team wrote.
According to the researchers, existing classical methods in computational chemistry are not accurate and the costs increase as the scale of calculations increases.
Quantum computing, which harnesses quantum mechanics to perform computational tasks that classical computers cannot handle, could potentially be used to overcome current challenges in drug discovery.
“However, to date, the application of quantum computing in drug discovery has been primarily limited to proof-of-concept studies, with minimal integration into real-world drug design,” the team said.
In answer, the team developed a hybrid quantum computing pipeline targeted at real-world drug discovery, and were able to validate this using two case studies dealing with real problems in drug design.
“Our findings demonstrate the feasibility of integrating quantum computing pipelines into real-world drug design workflows,” the researchers said.
The team sought to solve two key challenges in drug discovery: determining the energy needed to cleave, or break, bonds in prodrugs — drugs that go from inactive to active in the body — and performing simulations of covalent bonds, chemical bonds in which atoms share electrons.
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Prodrugs are an important part of modern pharmaceutical research because they are “active only in specific locations in the body, reducing the risk of side effects and leading to safer, more effective treatments,” the researchers said.
One strategy to activate these drugs is the cleavage of carbon-carbon bonds, and according to the team, calculating the energy barrier for breaking these bonds is “crucial” because it determines whether it will occur naturally in the body.
To determine whether a quantum computing pipeline could be used for this study, the researchers looked at beta-lapachone, a precursor to an anti-cancer drug.
They compared their computing results to a 2022 paper that used classical computing methods to determine the energy barrier in parallel with laboratory experiments.
The quantum computer-based analysis was consistent with previous studies, with both finding that the drugs had the potential to react spontaneously within the organism.
“Our findings demonstrate the efficacy of quantum computing and also the versatility and plug-and-play advantages of our pipeline,” the researchers wrote.
In the second case study, the team sought to examine the activity of another anti-cancer drug, sotorasib, known as a KRAS (Kirsten Rat Sarcoma) inhibitor, which blocks a specific KRAS gene mutation, G12C.
Finding drugs to treat this cancer gene mutation has been difficult because they need to form a covalent bond with the target to block the mutation.
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To study drug-target interactions, quantum mechanics and molecular dynamics simulations were used – simulations that are essential for post-design drug validation. The team used a hybrid computing approach, starting with a quantum emulator and then moving to a quantum computer.
After conducting hybrid quantum computing validation on sotorasib and the targeted mutation, the team observed that a strong covalent bond formed between the two, which may provide insight into the efficacy of the drug.
“This understanding will be crucial for the rational design of future inhibitors targeting similar mutations,” the researchers said, adding that it would underpin future advances in the speed and precision of drug discovery using quantum computing.
“In this study, we establish a model pipeline that enables quantum computers to tackle real-world drug discovery problems,” the researchers said.
“The generality of our pipeline highlights its potential as a fundamental tool, providing researchers with a ready-to-use computational resource.”
This could be made available to drug design experts with no quantum computing knowledge, they said.
“Democratizing access to this advanced pipeline lays the foundation for greater collaboration within the scientific community and accelerates the translation of the power of quantum computing into tangible therapeutic outcomes,” the researchers said.
He also said more work is needed to improve the accuracy of quantum computing methods for drug discovery, with one challenge being the current limitations of quantum computers, such as the length of time they can take to perform calculations and errors.
