June 24, 2022 PAO-06-022--NI-10
superconducting loops or ions hovering in electromagnetic fields (ion traps), the former can achieve quantum superposition, a state in which quantum bits (qubits) simultaneously exist in a probabilistic combination of those two states.1
Superposition enables the calculation of two problems at once. When multiple qubits are linked through the quantum effect referred to as entanglement, the number of calculations that can be performed increases exponentially with the number of qubits: 22 with 2, 23 with 3, and so on. As a result, if it were possible to construct a quantum computer with 300 qubits, such a machine could perform more calculations than there are atoms in the visible universe — almost instantaneously.2
Because quantum computers solve problems in a probabilistic manner, they take many different options into consideration simultaneously rather than sequentially, making it possible to process much more information much faster than conventional computers.3 Problems that would take classical computers several years or more to solve can be calculated by quantum computers in seconds.
Currently, quantum computers operate using a hybrid algorithm (referred to as the variational quantum eigensolver) in which classical computers complete the bulk of the calculations, and quantum processors take over at the point when the problem becomes too difficult.2 One question that needs to be resolved is what design will offer the most effective and scalable solutions for quantum computing: supercomputing as developed by Google and IBM or ion traps as presented by IQM and Honeywell, which use neutral atom or photonic mechanisms for creating qubits.5 The former are faster but prone to more errors. Some believe that these different solutions will ultimately prove appropriate for different applications.
With its ability to rapidly solve highly complex problems, quantum computing is overcoming the limitations of classical computers, and eigensolver is expected to enable significant advances in fields such as national security and energy research, as well as the development of new materials and pharmaceuticals, including personalized medicines.6 It has already been shown to provide advantages for neural networks and generative adversarial networks (GANs).5
The operation of quantum computers using quantum mechanics makes them ideally suited for simulating chemical systems, such as drug molecules and their interactions with proteins.1,4,6 The potential to simulate very large and complex molecules could have a tremendous impact on drug discovery and development efforts. That is because molecules are quantum systems based on quantum physics. Quantum computers should therefore be able to more effectively predict and simulate molecular structure, properties, and behaviors, including interactions at the atomic level.1,7
For instance, quantum computers will be better able to take into account the quantum effects of ions and metals that most current machine learning algorithms ignore for simplicity and standardization. They can also factor in the dynamic motions of proteins and how their shapes evolve over time, which also is typically not included in models used on classical computers.8
Current quantum computers can perform calculations for molecules with five to 10 atoms. Because most small molecule APIs comprise 30 or more atoms, calculations are performed on molecule fragments, and then density matrix embedding theory is applied to understand the entire molecules.2 As advances in quantum computing technology are achieved and stable computers with many more qubits are realized, it will be possible to simulate ever more complex molecules — including biologics.
QuPharm alliance, which provides members the opportunity to collaborate on the development of quantum computing solutions for pharmaceutical applications.9
In fact, in a future-casting exercise conducted in 2020, the Pistoia Alliance, a consortium of pharmaceutical information technology managers covering laboratory computation broadly, clearly identified quantum computing as a future technology with significant potential to impact the pharmaceutical industry.10 By late 2020, QuPharm had already identified 24 possible uses of quantum computing in research labs, including target identification, lead discovery, lead optimization, and clinical development.11
QuPharm is collaborating with both the Pistoia Alliance and the Quantum Economic Development Consortium (QED-C), a group launched in 2018 with funding from the U.S. National Institute of Standards and Technology.12 In addition, through funding made available by Innovate UK’s Industrial Strategy Challenge Fund (ISCF), QuPharm intends to build and deliver a full-stack quantum computer for pharmaceutical drug development.11
Many companies are getting directly involved with quantum computing as well. In a poll conducted during a webinar hosted by QuPharm, QED-C, and the Pistoia Alliance in late 2020, 82% of participants indicated that they believed quantum computing would impact the pharma industry within the next year. In addition, nearly a third had plans to evaluate quantum computing within the next year, and another third anticipated evaluating the technology in the near future.13
computer-assisted drug discovery (CADD).1 Current approaches rely on non-quantum-computing-based tools, such as molecular dynamics (MD) simulation and density functional theory (DFT), and are limited in terms of molecular complexity and cost. Quantum computing has the potential to allow cost-effective and timely analysis of larger, more complex molecules.14 As such, quantum computing would allow researchers to “fail earlier” and thus accelerate the development of drug candidates with greater likelihood of success.1
Quantum computing could also increase the accuracy of CADD through not only better modeling of complex interactions but also analysis of the structural flexibility associated with many more molecules simultaneously.1 When combined with ML, quantum-based CADD could help more rapidly uncover new structure–property relationships and enable novel drug designs.
Specific applications of interest to drug developers include target identification and validation, hit generation and validation, lead optimization, protein structure prediction, and protein engineering and design.1,14 The ability to rapidly analyze complex data sets should also allow better utilization of high-throughput technologies.6
Currently, companies such as ProteinQure are focusing on molecular similarity, protein structure prediction, and protein design, but quantum computing is expected to be applicable to most CADD workflows, including quantum chemistry problems and molecule docking.15 Indeed, many believe that quantum processing has the potential to disrupt drug discovery, providing great value by dramatically reducing the >90% failure rate in preclinical and clinical stages.
Perhaps most importantly, beyond accelerating drug discovery and development, quantum computing has the potential to enable innovative approaches to problem solving, opening up the possibility for developing and manufacturing new medications that were not previously thought possible.16 As an example, in the field of precision medicine, quantum computing could potentially enhance motif discovery and prediction and enable genome-wide association studies and de novo structure predictions. Another possibility is the development of organic digital twins for evaluating patient responses to specific medical procedures, medicines, and medical devices.
Quantum computing is expected to have applications that go well beyond drug discovery. Better linkage of data through more effective “topological data analysis” could potentially enable the study of unknown connections between cells and help to elucidate unknown disease mechanisms.1 Quantum computing could also be applied to the optimization of clinical trials through patient identification and stratification and population pharmacogenetic modeling.1,3
More rapid analysis of complex data sets with multiple interactions offers the potential to help optimize synthetic route development through more accurate determination of reactions rates and optimization of catalytic process for chemical APIs, increase the efficiency of formulation development, improve large-scale manufacturing, and enhance supply chain modeling.5,14 It could also have business-related applications, such as in solving financial risk-optimization problems.13 The use of real-world data and real-world evidence will also benefit from the greater ability of quantum computing to analyze large volumes of complex data at fast speeds.17
“noisy intermediate-scale quantum” (NISQ), or probabilistic computers that produce error-prone results that can be used in the near-term as long as the uncertainty is taken into account. Over the longer term (beyond 2030), fully error-corrected quantum computing is expected to be achieved and widely adopted in the pharmaceutical industry.
In addition, given the current state of quantum computing technology and the anticipated rate of its development, it is generally expected that the first applications will actually be hybrid solutions in which classical computing is linked with quantum computing.1,13,15 A move to solo quantum computing applications will first require advances in infrastructure (e.g., super-cool facilities) and computing hardware.6,17 Off-the-shelf systems are also needed that can be used by nonspecialists.15
Researchers will also need to learn how to translate drug discovery problems into quantum computational problems and ascertain how to leverage quantum computing technologies within existing drug discovery and development workflows.12
are focused on drug discovery, while others emphasize their quantum computing capabilities.3
Quantum Brilliance is developing diamond-based quantum computers that are smaller than mainframe quantum computers and operate under less extreme conditions than most quantum systems. The company has also developed molecular dynamics simulations using its computers that can accelerate drug design.7 Qubit Pharmaceuticals uses a high-resolution physics model that takes into account polarization charges, protein flexibility, and allosteric modulation, combined with fast sampling and the application of statistical physics, for insights into appropriate chemical spaces to identify attractive drug candidates.8 Others pursuing similar approaches include Schrödinger Inc. and Silicon Therapeutics.
Cambridge Quantum Computing, meanwhile, is developing software to evaluate protein binding, determine crystal structures and optimum synthetic routes, and predict how molecules will react to different stimuli.2 Zapata Computing also engineers software, but it doesn’t stop there — it runs those algorithms to generate answers for biopharma companies wanting to know which candidates have the greatest likelihood of success, which patients should be recruited into clinical trials, and the optimum strategies for distribution of approved drug products.5
Other companies that are applying quantum calculation methods in combination with classical computing techniques to support pharmaceutical discovery and development efforts include ApexQubit, Aqemia, CreativeQuantum, Entropica Labs, GTN, Hafnium Labs, Kuano, Menten AI, Molecular Quantum Solutions, NetraMark, Pharmacelera, PharmCADD, Polaris Quantum Biotech, ProteinQure, QSimulate, Riverlane, Roivant Discovery and XtalPi.5,19
Additionally, companies focused solely on quantum computing solutions are partnering with some of the above companies to provide solutions targeted to the pharmaceutical industry. Examples include 1Qbit, D-Wave, Qu&Co, Rigetti Computing, Seeqc, and Xanadu. It should also be noted that the large tech companies, including Amazon, Microsoft, IBM, and Google, are involved in the space as well.
the former’s graph-based optimization algorithm to review molecule matches, predict positive therapeutic effects, and assess side effects in early-stage discovery.20 Boehringer Ingelheim is working with Google to explore the use of quantum computing to enhance molecular dynamics simulations.21 Roche is collaborating with Cambridge Quantum Computing to simulate quantum-level chemical interactions with the goal of discovering new Alzheimer treatments and, eventually, candidates for other diseases.22 Pfizer is involved in a recent strategic research collaboration with XtalPi to use quantum computing to perform crystal structure prediction rapidly enough to use this technology, which used to take up to four months using classical computing, on almost all of Pfizer’s’ small molecule candidates.23
Merck partnered with Seeqc with the intention of using the latter’s quantum computers via the cloud.12 Fujitsu, meanwhile, is employing a new quantum-inspired platform to significantly improve the speed and quality of small molecule lead discovery.24
drug development, manufacturing, and the supply chain, with large scientific gains anticipated in drug discovery in the long term.25
Furthermore, the combination of AI and quantum computing is expected to expand the potential impact of each technology on its own. “The power of AI–QC [quantum computing] makes accessible a range of different pharmaceutical problems and their rationalization that have not been previously addressed due to a lack of appropriate analytical tools, demonstrating the breadth of potential applications of these emerging multidimensional approaches. As such, a comprehensive knowledge of the underlying pillars is imperative to extend the landscape of applications across the drug life cycle,” according to a team of experts from Portugal.26
Notably, Amazon, IBM, Google, and Microsoft have already launched basic quantum-computing cloud services and expect that fully functional quantum computers will be available as soon as 2030.27 It is therefore not surprising that McKinsey estimates global pharma spending on quantum computing in R&D to be in the billions by 2030.1
Dr. Challener is an established industry editor and technical writing expert in the areas of chemistry and pharmaceuticals. She writes for various corporations and associations, as well as marketing agencies and research organizations, including That’s Nice and Nice Insight.