by “Quantum computing, with its superior computational capabilities compared to classical approaches, holds the potential to revolutionise numerous scientific domains, including pharmaceuticals,” the team wrote.
Existing classical methods in computational chemistry are not exact – and their cost goes up as the scale of computing grows, the researchers said.
“However, in the current landscape, the involvement of quantum computing in drug discovery is primarily restricted to conceptual validation, with minimal integration into real-world drug design,” the team said.
In answer to that, the team has developed a hybrid quantum computing pipeline targeted for real-world drug discovery, which they were able to validate using two case studies that addressed real problems in drug design.
“Our results demonstrate the potential of a quantum computing pipeline for integration into real world drug design workflows,” the researchers said.
The team sought to carry out two critical tasks in drug discovery: determine the energy needed to cleave or break bonds in a prodrug – a drug that turns from inactive to active inside the body – and the simulation of covalent bonds, a chemical bond where atoms share electrons.
One strategy to activate these drugs is the breaking of carbon-carbon bonds. According to the team, the calculation of an energy barrier for cleavage of these bonds is “crucial”, as it determines whether it can happen spontaneously within the body.
They compared their computing results with a paper from 2022 that used classical computing methods to determine the energy barrier alongside laboratory experimentation.
Analysis using the quantum computer agreed with the previous study, with both analyses determining the drug could undergo a spontaneous reaction within biological organisms.
“Our results demonstrate the effectiveness of quantum computing … as well as the versatility and plug-and-play advantages of our pipeline,” the researchers wrote.
In their second case study, the team sought to determine the activity of another anticancer drug, sotorasib, known as a KRAS (Kirsten Rat Sarcoma) inhibitor, which inhibits a specific KRAS gene mutation, G12C.
Finding medication for mutations of this oncogene has been a challenge, as it needs to form a covalent bond with the target in order to inhibit it.
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Quantum mechanics and molecular mechanics simulations – vital simulations in post-design drug validation – were used to examine the drug target interaction. The team used a hybrid computing method, meaning they started with a quantum emulator before moving to a quantum computer.
After performing hybrid quantum computing validation on sotorasib and the target mutation, the team observed that a strong covalent bond formed between them – which could offer insight into the drug’s efficacy.
“This understanding is pivotal for the rational design of future inhibitors targeting similar mutations,” the researchers said, adding that it would underpin future advancement of the speed and accuracy of drug discovery using quantum computing.
“In this study, we have established a model pipeline that enables quantum computers to tackle real-world drug discovery problems,” they said.
“The universality of our pipeline highlights its potential as a foundational tool, empowering researchers with a ready-to-use computational resource.”
They said even drug design experts without a background in quantum computing would be able to use it.
They also said more work was needed to improve the accuracy of quantum computing methods for drug discovery. One challenge is the current limitations of quantum computers, such as longer computational time and errors.
