Pharma looks for quantum leap in drug development

• Making drugs faster and cheaper with quantum computers could solve one of pharma's most vexing problems and simplify a process that now can take decades and cost billions.

Where it stands: Today's quantum computers are largely used for research problems — modeling materials or chemical reactions — and aren't day-to-day tools for drug discovery and design.

• But pharma and tech companies are hoping they can soon provide a more efficient and accurate way to analyze an ever-growing amount of data.

How it works: Classical computers process data in only two states — ones and zeroes — while quantum computers harness the properties of subatomic particles and use qubits, which can represent ones and zeroes simultaneously, to in theory process information in exponentially more powerful ways.

• That effectively allows quantum computers to consider multiple solutions to a problem faster than a conventional computer.

Quantum computers should be able to precisely simulate molecules and the way they interact, which current classical computers can at most approximate.

• Accurate predictions of the behavior of the thousands of atoms in a proposed drug and its environment would be a game-changer.
• Google and German pharma giant Boehringer Ingelheim are collaborating on using quantum computing for molecular simulations, and French quantum computing startup Pasqal is partnering with Qubit Pharmaceuticals to tackle the problem.
• But today's quantum devices are error-prone and can generally only simulate simplified models that classical computers can also handle.

Some pharma companies are instead focused on using quantum computing for another type of problem: optimization to find the best solution to a problem that can be solved many ways.

• Key opportunities for optimization algorithms include determining how well a drug interacts with its intended target in the body, programming clinical trials and managing supply chains and sales.
• "Optimization problems are really something that we can do today," Herman Van Vlijmen, head of computer-aided drug design at Johnson & Johnson, which is collaborating with Pasqal, said in an interview in July.

Zoom in: Moderna, in partnership with IBM Research, is experimenting with using quantum computers to predict how messenger RNA molecules fold when nucleotides that aren't next to one another interact.

• The loops, hairpins and other features of RNA structure influence how proteins are synthesized and genes are expressed — critical information when designing an RNA-based drug.
• Conventional computer modeling can provide solutions to the problem but Moderna's preliminary work on short mRNA sequences suggests quantum computing can accurately predict structures as well.
• And the nature of quantum computers means they can consider far more of the vast number of possible configurations.

"We can do great things today without a quantum computer to attack that," says Wade Davis, vice president of computational science at Moderna.

• "But there is the potential — because of the nature of quantum — to do some things in a different way for those type of problems."

Between the lines: Quantum optimization is as much — if not more — about sussing out many possible solutions when presented with different goals or constraints as finding a solution faster.

• It could be about getting the same immune response from a vaccine at a lower dose or with a vaccine formula that could be kept at room temperature, Davis says.
• Quantum is "giving us a full searchlight in that giant, multidimensional space and to shine it in different places than we [currently] are. We're really, really, really scratching the surface of the possibilities," he adds.

Yes, but: Quantum optimization still has hurdles to clear, including increasing the number of qubits in a quantum device so it can take on more complex problems and coming up with ways to correct errors in the sensitive systems.

• It's also still unclear which optimization problems quantum algorithms can actually solve more efficiently than classical computers.

What to watch: Some quantum computing experts predict the fusion of AI and quantum computing could make big advances in the coming year.

• The idea is to use quantum computing to power AI models and overcome classical computers' limits in processing large datasets or running simulations.
• "There is reason to remain cautiously optimistic about the power of quantum computing for machine learning," Ryan Babbush, who leads quantum algorithms and applications research at Google, tells Axios in an email. That could be especially relevant for drug design efforts to develop molecules that achieve a desired function.

The bottom line: "It's really understanding your problems and getting them ready for quantum. That's where we're at," Davis says.