How quantum computing can benefit drug discovery 

Listen to this article on the DDW Podcast:

Brad Loren, Matthew Marrone, and Christopher Singer, of intellectual property and technology law firm McAndrews, Held & Malloy, on quantum computing and its benefits to drug discovery. 

Explaining quantum computing and its promise 

Before we explore how QC can benefit drug discovery and drug development, let us first take a step back and explore what QC actually is, and what it promises to do. QC is a computational branch of technologies broadly described as quantum information science (QIS). QIS is a growing area that employs the near-magical properties of quantum mechanics with a variety of technologies, such as computing and sensing, among others.  

At its core, quantum computing exploits the phenomena of quantum mechanics to analyse, interpret, and employ enormous datasets to solve complex problems. As such, it’s poised to drive revolutionary advances across a vast array of essential areas — from national security to energy research to the development of new materials, pharmaceuticals, and even personalised medicines.  

The pioneers of computing could only imagine what modern technologies could achieve. But even as the computational capabilities of classical (eg, binary or digital) computing continues to progress, a variety of complex computational problems remain out of reach. As alluded to by Nobel Laureate Dr. Richard Feynman, who is considered a pioneer in the field of QC, a classical computer lacks the capacity to ‘imitate’ quantum systems.  

Just as the scientific world was turned on its head when the classical understanding of physical systems was upended by early quantum theorists, constraints of classical computing are being overcome by QC.  

As with any scientific breakthrough — and QC promises nothing short of a revolution — the technology supporting and explaining QC is neither easy to describe nor grasp. However, as the many applications promise such significant benefits to computationally complex problems, even untested, theoretical solutions are hard to ignore. Specifically, in the area of drug discovery and pharmaceutical development, QC promises to provide faster and less costly options that deliver better health outcomes than the best current (and even future) conventional computers. 

Quantum and drug development 

It is no secret that drug discovery and drug development are time-consuming and costly. One recent estimate indicated that the pharmaceutical industry’s research and development (R&D) spending accounted for greater than 20% of global R&D spending across all industries.  In recent years, pharma’s R&D budget has exceeded 25% of net revenues across the entire pharmaceutical industry. A variety of factors contribute to the high R&D costs.  

One factor is that drug development has an inherently high failure rate. Even using state-of-the-art technology, only about 12% of drugs that are promising enough to enter clinical trials achieve FDA approval. A far larger number of drug candidates fail at earlier stages of the development timeline and never enter clinical evaluation. As a result, a recent study estimated the average cost of taking a new drug from R&D to FDA approval was over $1 billion.  

This is where QC may provide great advantages: First, rapid analysis of complex datasets can change the way scientists and engineers utilise high throughput technologies in both biological assays and chemical synthesis. Second, QC’s ability to model complex molecular structures will provide a better understanding of how drugs – both small molecules and biologics – will behave long before they reach preclinical and clinical phases of drug development. We further explore these opportunity areas below.  

One important thing to note is that QC will not be taking over every task performed by classical computational methods – at least not anytime soon. A likely future is that QC will be coupled with classical computational technologies, as well as technologies such as machine learning (ML) and artificial intelligence (AI). 

High-throughput experimentation 

Pharmaceutical companies are constantly seeking ways to improve efficiency and decrease costs at all stages of R&D. High throughput experimentation (HTE) is an area that plays an increasingly pivotal role in drug development. As the name implies, high throughput technologies allow scientists to execute and analyse large numbers of experiments in a short timeframe. HTE platforms are routinely employed for a variety of biological assays and, more recently, HTE has progressed significantly in its ability to execute chemical reactions as well.  

In the biological space, HTE platforms have opened the door for the broad screening of drugs against a vast array of biological targets. This allows researchers to better understand the diseases being studied and to identify the most promising candidate molecules at the earliest possible stage of R&D. In recent years, HTE’s value in screening chemical reactions has also been established. More specifically, HTE has been employed to improve the efficiency by which candidate molecules can be synthesised. This has value not only in early R&D, but also in later phases of drug development.  

For example, in early R&D, HTE can be employed for the rapid small-scale synthesis of a large number of molecules to be used in biological assays. In other words, high throughput chemical reaction systems can be utilised to synthesise the molecules needed to make the most out of high throughput biological assays.  

At later stages of development, HTE also has value in optimising the synthesis of particular molecules of interest. For example, once a promising target drug molecule is identified from biological assays, HTE can again be implemented to fine tune the most efficient synthesis of that molecule by screening a vast array of reaction types and conditions (eg, solvent, stoichiometry, catalyst, etc). 

HTE also affords us the ability to maximise the potential for serendipitous discoveries. Of course, even the most brilliant researchers are often surprised by experimental results. HTE gives scientists the added bandwidth to run experiments they may not have attempted due to time constraints of manual workflow. Serendipity has, and always will, play a role in scientific discovery. As such, it is never possible to predict the results with absolute certainty for any given experiment. That is, after all, why it is called an experiment. By providing the opportunity for researchers to run experiments that likely never would be attempted in the absence of HTE, the opportunity to discover unexpected results greatly improves. Some experts have referred to this as “accelerated serendipity.”  

HTE gets attention for the staggering numbers of experiments that can be run in a short amount of time on the order of thousands of screening reactions per day (a number that continues to rise).  However, running thousands of experiments per hour has little value if the data from these experiments cannot be analysed and interpreted at an equally rapid pace. As the number of experiments and the complexity of the data collected increases, the computational task of analysing the data and generating actionable results becomes more and more daunting. In addition to the sheer number of experiments being run, the data from each individual experiment can have a number of experimental variables and outcomes that need to be evaluated. Therefore, the result of a high throughput experiment can be a complex matrix of data that even a powerful classical computer on its own won’t be able to tackle in an efficient or useful manner. 

This is where the advantages of QC provide an opportunity. By manipulating the quantum effects of superposition and entanglement, QC can employ quantum bits (qubits) to analyse complex and large datasets not just faster than conventional computers, but in a fundamentally more efficient way. Through superposition and entanglement, networks of qubits can simultaneously test multiple scenarios and combinations of results. For example, QC can access vast libraries and quickly identify promising results (as well as identifying unlikely winners) to analyse and compare data to find patterns that would take impractical amounts of time and computational resources using conventional computers.  

QC as key to complex molecular modelling 

Computational chemistry and molecular simulations are a particular area of excitement for QC researchers. This is because QC promises to deliver the ability to more accurately simulate complex molecular structures. Such a capability would improve the efficiency of the drug discovery process by providing better results in shorter timeframes, thereby allowing scientists to more reliably select the most promising drug targets that should be explored experimentally. 

Computational methods are nothing new in drug discovery. Pharmaceutical companies have long utilised technologies such as molecular dynamics (MD) and density functional theory (DFT).  More recently, technologies such as AI and machine learning have been employed at various stages of drug development. While these tools add value to the drug discovery process, and will continue to add value, they also have their limits. Most notably, drug discovery invariably involves complex molecular structures. While the most common drugs are referred to as “small molecules,” small does not infer simplicity. Small molecule drugs often have incredibly complex structures. As such, assumptions often need to be made in order to complete MD and DFT calculations.  

Additionally, there is an increasing desire to tackle the daunting task of simulating biological structures. For example, the ability to reliably simulate protein folding, and how a drug interacts with a particular protein, could streamline the drug selection process and reduce the number of experiments needed to reliably select targeted drug molecules. Biologics are also becoming increasingly important therapies and drug discovery targets. However, biologics bring with them even more molecular complexity than small molecule drugs and are generally more expensive to manufacture. 

All of this is not to say molecular simulations with classical computing don’t currently play an important role in drug discovery. They certainly do. However, the assumptions and simplifications that need to be made in order for classical computers to complete computations of molecules of interest – both small and large – limits the utility of computational methods in drug discovery. QC could change all of that. 

The unique benefits of QC that derive from superposition and entanglement offer the ability to draw comparisons across large and often unrelated data libraries. Due to the ability to simultaneously analyse and cross-analyse different data sets from a variety of sources, multiple structures of drugs, including biologics, can be tested in different forms, combinations, and even under different conditions.  

This is partially the case due to the nature of the materials being investigated, namely atoms, electrons, and molecules. As inferred by Dr. Feynman, a quantum-based machine is best suited to model a quantum environment. Thus, small molecule modelling and simulations of such systems, being fundamentally driven by quantum mechanics (such as electron behaviour), are well-suited for QC, while avoiding techniques that today can lead to fewer quality results, such as broad approximations for electromagnetic interactions between specific atoms or electrons.   

The road ahead 

Of course, none of this means that the path ahead is clear. Harnessing the power of quantum mechanics is a complex and delicate task, and many challenges remain.  

With technological advancements often come business and legal challenges. Some areas that could benefit the most from quantum computing are subject to significant regulatory scrutiny, and that certainly includes the pharmaceutical industry. Before full scale adoption of quantum technologies, regulators and lawmakers will seek a level of understanding of these tools and the products they generate. To ensure the development of QC accelerates at pace, those committed to a quantum future should anticipate the challenges of this possible future and seek to address them in early stages.  

On the technological front, major innovations are still needed to make QC a reality, such as hardware that faithfully mimics quantum mechanics and algorithms specifically designed for quantum hardware. For instance, qubits are delicate and maintaining superposition and entanglement, the properties that give QC its power, often requires complex and carefully controlled environments (e.g., superconducting circuits at extremely cold temperatures; so called ‘ion-traps’ employing lasers or electromagnets in isolated vacuum chambers) . These extreme conditions are needed to ensure the qubits (i.e. pairs of qubits) maintain their quantum behaviour and do not ‘decohere’ (when the state of one or both of a pair of qubits is disturbed). If either superposition or entanglement are broken, the quantum operation fails .  

Adopting a new quantum framework offers innovators and entrepreneurs a rare opportunity to shape how industries employ and incorporate QC to help their businesses grow. As QC becomes more widely used, protocols will develop, standards will be set, and new applications and products, today unimaginable, will surely emerge. Those who tackle these hurdles early are likely to become leaders in shaping the quantum future.   

As interest and investment grows, competition for talent and resources will intensify. Protecting intellectual property, such as through patents and trade secrets, provides an important step in ensuring your innovations remain valuable as more companies and industries embrace this new technology.   

But until researchers are able to make reliable, error-free quantum bits, or qubits, widely available, some of the promise of QC will remain out of reach. So what should pharmaceutical companies do today to prepare? A good first step is performing an internal assessment to determine the potential impact of quantum computing on the company or industry and assembling a team of advisors, including technical, business, and legal counsel, who can help develop strategies and approaches that will enable the enterprise to be quantum-ready.  

The quantum revolution may usher in a new era of discovery beyond the strictures of today’s thinking. After all, it is clear that quantum computing promises to revolutionise the way drug discovery is conducted. Those who prepare for the revolution will reap the rewards. 

Volume 23, Issue 1 – Winter 2021/22

About the authors 

Matthew Marrone is a Partner at McAndrews, whose practice includes all areas of intellectual property law. He has expertise in the electrical, software, communications, and computer system technology areas. Marrone helps inventors, corporations and academics protect their innovations in emerging technologies. 

Christopher Singer, Ph.D, is Shareholder at McAndrews, and concentrates on securing patent rights in life science technologies such as biotechnology and pharmaceuticals. His experience includes patent prosecution and global patent portfolio management, including due diligence, patent validity and infringement. 

Bradley Loren, Ph.D., is an Associate at McAndrews, where he focuses on intellectual property litigation and prosecution. He has worked on a wide range of matters and has experience in motion practice and advanced legal research for cases in a variety of technical areas. 

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