Quantum Computing Applications in Drug Discovery and Healthcare

  

1. Introduction to Quantum Computing

Computers and computer-based systems are used in just about everything, including but not limited to everyday applications, banks, hospitals, pharmaceuticals, etc. During the early stages, computers were made using electromechanical switches which were bulky, slow, and involved many mechanical failures. This changed with the invention of transistors which changed the world of electronics. Transistors are small, fast, and inexpensive compared to mechanical switches and are capable of more complex calculations. A similar transition is about to happen in computing with the development of quantum technologies. Quantum computing is the generation of computational systems based on the principles of quantum mechanics. Quantum computing is fast, and complex and can model the quantum system. Quantum systems consist of quantum states manipulated using quantum gates resulting in quantum algorithms. Wave functions characterized by probability amplitudes described quantum states. Two crucial characteristics of quantum states are superposition and entanglement. A quantum bit or qubit is the basic unit of quantum information analogous to a bit in classical computing. Classical bits are either 0 or 1 whereas qubits can exist in both 0 and 1 states simultaneously. This property is called superposition allowing multiple calculations to be processed simultaneously (Shams et al., 2023). Qubits can be represented using different physical systems including photons, ions, atoms, and superconductors. Quantum computers can be built using these qubit technologies. The development of quantum hardware technologies has progressed rapidly in recent years with advancements in experimentation, theory, and engineering.

Quantum computing technology still has a long way to go, but the technology readiness level is increasing rapidly. From academic research, proof-of-principle experiments, and prototypes quantum computing technology is entering into translatable research and development efforts in quantum start-ups and industries. Big tech companies and governments are investing billions of dollars and establishing in-house quantum research groups, considering quantum computing the next frontier of computing and a game changer that can revolutionize the industry and society. The socio-economic impact of quantum computing technology is expected to be similar to or greater than that of classical computing. The applications of quantum computing technology in the pharmaceutical industry are in drug design and drug discovery. Currently developed quantum algorithms that exponentially outperform the fastest classical algorithms include quantum algorithms for linear algebra problems and quantum simulations of quantum systems under certain conditions (F. Flöther, 2024).

2. Fundamentals of Drug Discovery

Since the discovery of penicillin, drug discovery and development has become one of the most complex and challenging tasks. Several processes need to be performed sequentially or in parallel to bring a new drug to the market. Besides feasibility and commercial factors, a minimum of safety and efficacy testing needs to be performed on new drugs before market approval can be obtained from regulatory authorities (Li et al., 2024). Testing in a living organism, e.g., in vivo pharmacokinetic studies, requires significant time and resource investments, costs several million dollars, and can take several years. Moreover, a very high failure rate is observed during clinical trials, up to 80% for Phase III trials, where the most expensive studies are performed. To mitigate these high attrition rates, attempts are being made to use computational methods to facilitate drug discovery and development.

Many stages can be identified, each with specific biological and chemical processes at play, where drug discovery and development efforts typically focus. During the target identification phase, a biological target is sought, usually a protein, whose modulation can lead to the desired biological effect. A good target candidate should elicit a strong effect in a disease state but have minimal effect on normal physiological processes. The next phase, lead discovery, involves screening small molecules from vast chemical libraries, either experimentally or in silico, that bind to and preferably inhibit the action of the target protein. Several free energy-based methods exist to rank the small molecules concerning their binding affinity to the target, the most commonly used one being molecular docking. With a hit or several hits in hand, secondary pharmacological profiling is performed to explore the inhibitory effect of the small molecule on its target. An important concept here is that of the metabolic stability of a drug. Enzymes responsible for the degradation of small molecules, usually cytochrome P450 isoforms, are highly abundant in the liver, and an exhaustive in vitro profiling against these enzymes is performed to minimize the chances of molecules failing due to poor pharmacokinetic properties later on.

3. Challenges in Traditional Drug Discovery

Drug discovery is essential but plagued with hurdles. The traditional methodology is lengthy and costly, taking an average of 10-15 years and over USD 2 billion to bring new drugs to market. This process is then streamlined through research and clinical trials, where many complications arise. Out of many candidates that enter animal studies, only 10% advance to human trials, and of those, only about 12% succeed. Most failures are attributed to issues based on the candidate’s efficacy, safety, or toxicology (Li et al., 2024). Consequently, the pharmaceutical industry has seen a decline in newly approved drugs, despite increasing R&D expenditures. This failure cascade is exacerbated in the discovery of complex polypharmacology drugs, as multiple targets must be considered. Current in vitro and in vivo methodologies cannot predict drug-target interaction profiles due to an inability to consider the entire biological network.

Biological systems are intricate, as they involve a multitude of constantly interacting entities at different scales. Thus, brute-force approaches can neither replicate natural development nor reliably extrapolate from small-scale experiments. As a result, many processes can take aeons and must be sped up using abstract models, which generate their hurdles. Furthermore, regulatory hurdles and ethical considerations impede progress, especially in areas that could significantly affect public health and are routinely grossly misrepresented by the media. Models of good laboratory practice (GLP), though mandatory for preclinical studies before human trials, add another layer of complexity to animal experiments that can critically affect outcomes. Besides drug discovery, other research areas have similar issues, generating awareness of the dire need for more efficient alternatives. Having dissected the complications of the traditional model, attention is turned toward an introduction to quantum computing, outlining how it might provide solutions to these pervasive issues in drug discovery.

4. Quantum Computing Basics

Quantum Computing Basics is a self-contained overview of the principles and terminology that define quantum computing. Fundamental concepts necessary to fully understand all that follows are covered, such as qubit, quantum gate, and quantum circuit, accompanied by all relevant pictorial representations. The quantum principles on which quantum computing relies most heavily – superposition and entanglement – that facilitate a quantum computer’s unique capabilities compared to its classical counterpart are also deeply addressed. The difference between classical and quantum computing paradigms is clarified, particularly concerning the latter’s exponentially faster processing power advantage due to a quantum computer’s use of probabilistic, rather than deterministic, states of information (A. Cordier et al., 2021). Furthermore, a quantum computer’s key technological components involved in its construction – quantum processor, quantum gate, quantum circuit, qubit, measurement, and error correction – are also described.

The state of quantum technology evolution and its ongoing development in the industry and academia landscape is outlined, along with the currently up-and-running quantum computing computers available as cloud services and in-house setups, along with their anticipated applications for the future. All here presented information provides a solid groundwork for everything related to drug discovery and healthcare in the sections that follow. Hence, a reader should be able to appreciate the complexity of quantum algorithms and their applicability after absorbing this straightforward overview (Shams et al., 2023).

5. Quantum Algorithms for Drug Discovery

As proof-of-principle demonstrations become more common in quantum computing benchmarks, particularly in near-term machines, industries that have been hampered by classical computational limitations may begin to focus their attention on the potential benefits of quantum computing. One such industry is pharmaceuticals, where the development of new drugs is a complicated and costly process involving the modeling of molecular interactions. Drug design entails the search for a molecule that best satisfies a specific set of requirements, typically involving the optimization of numerous structural and energetic properties—often referred to as a “fitness landscape.” Such problems are NP-hard when formulated as abstract mathematical problems (Li et al., 2024). Fortunately, certain classes of quantum algorithms can substantially outperform classical approaches for NP-hard problems, including quantum approximate optimization algorithms (QAOA).

Nevertheless, even outside the search space, there are many computationally intensive tasks associated with drug design and discovery that could substantially benefit from quantum computing. Classically modeling the ionic and electronic structure of molecules in a system is at the heart of many computational chemistry, material science, and drug discovery techniques. Unfortunately, the required computational scale of classical modeling techniques grows exponentially with the accuracy of the description, and state-of-the-art implementations currently reach molecular systems of only a few hundred atoms. On the other hand, quantum systems naturally encode chemical interactions, and quantum algorithms, such as the variational quantum eigensolver (VQE), have been proposed to take advantage of this insight. Quantum computing-enhanced drug discovery is thus modeled to quantum approaches to the two most widely used computational chemistry techniques: molecular force field and quantum molecular wavefunction. Each technique is suitable for modeling certain classes of drug-discovery problems. The hope is that quantum computing can both alleviate the limitations of classical approaches and open the door to completely new methodologies.

6. Quantum Machine Learning in Healthcare

Quantum Machine Learning (QML) techniques applied to healthcare problems quantify the current state and recent advancements of QML in health-related contexts. Driven by the ever-increasing size of accessible data, there is significant interest in adopting or augmenting classical machine learning techniques with quantum-enhanced alternatives. These approaches can analyze large datasets, uncover hidden patterns, and make predictions about future outcomes that cannot be extracted using classical methods (F. Flöther, 2023). QML applications focus on health-related contexts posing significant opportunities and challenges. Furthermore, there is increasing interest in conducting feasibility studies and proof-of-concept experiments using near-term quantum hardware. QML techniques successfully applied to clinical or health-related data sets demonstrate the potential impact on patient care and healthcare practices. Applications such as diagnostics, predictive analytics, and personalized medicine highlight the transformative prospects of leveraging quantum advancements in healthcare (F. Flöther, 2024). This review supports planning the next steps in developing quantum algorithms for applications in health and medicine by outlining the current landscape and detailing case studies of successful quantum machine learning techniques applied to health-related use cases. QML algorithms raise the speed and efficiency of utilizing quantum algorithms in care applications compared to traditional machine-learning approaches. Despite the recent success of QML in healthcare studies, challenges remain in data acquisition/integration and algorithm development that need to be addressed before these techniques can be practically implemented. Each computational healthcare application illustrates the use case and summarizes key findings, algorithm types, and relevant quantum computing platforms through clinical proof-of-concept experiments. The rapid emergence of quantum computing in health and medicine creates a roadmap of quantum computing and quantum machine learning applications across the healthcare landscape from a science and clinical research viewpoint.

7. Case Studies and Applications

Presenting a selection of case studies and applications that address real-world quantum computing applications in drug discovery and healthcare issues, through worked examples from research institutions and companies using quantum technologies to tackle these problems. Each entry details the problem being addressed, how quantum is applied, and a summary of results, aiming to provide an overview of successful quantum applications. The goal is to evaluate how these applications enhance drug discovery, patient care, and efficiency in healthcare. These examples demonstrate current successes with tangible results, providing evidence that quantum solutions are practical and feasible. The broad range of applications is intended to illustrate how quantum computing can be used across a variety of segments within healthcare and pharmaceutical research. Additionally, these cases should serve as a motivation to consider new challenges in the quantum space or further develop existing solutions. Ultimately, it is hoped that this discussion will illustrate how quantum computing is poised to become an important part of the future of drug discovery and healthcare in general (Shams et al., 2023).

The first set of applications focuses on drug discovery, with one of the most pertinent uses of quantum computing in pharma research. From small biotech firms to global pharmaceutical companies, a selection of challenges that can be addressed with quantum computing, along with proof-of-principle case studies or applications already implemented is presented. Drug discovery consists of several complex stages, from target identification through hit finding, lead optimization, and preclinical development, each with its challenges. The emphasis here is on the earlier phases of drug discovery, where quantum computing methods have already been directly applied to key processes. That said, a few comments on broader drug discovery challenges and quantum computing’s applicability beyond the proof-in-concept examples considered are included (F. Flöther, 2024).

8. Ethical and Regulatory Considerations

The section on Ethical and Regulatory Considerations addresses the significant implications of quantum computing integration for drug discovery and the broader healthcare field. It stresses that as technologies advance, ethical dilemmas will arise, particularly those that touch on patient data and patient safety. This section builds on the previous analysis of the possible quantum applications and considers some associated regulatory needs and ethical implications. Here, laws and regulations that presently exist around data privacy and consent will be summarized, along with the more general need for regulation around the implementation of quantum technologies in clinical settings. Other topics discussed include the importance of maintaining a high standard in all research and the implications of the quantum-enhanced therapies for equitable access given the likely high financial costs associated with any quantum-driven innovation. Finally, consideration is given to the potential risks that could arise with the development and implementation of quantum computing, particularly about algorithmic biases or unintended consequences from newly developed workflows.

As quantum computing advancements accelerate, it is vital to ensure that all developments surrounding this technology’s application for drug discovery are performed with due consideration for ethical obligations. Therefore, stakeholder engagement—from small community outreach activities to wider public discourses—will be advocated to ensure that the path towards a quantum-enhanced integration of technology in drug discovery takes ethics into account. The widespread healthcare and medical applications of quantum computing create a complex arena where the dramatic possibilities of innovation must be balanced against obligations to act ethically. Many similar considerations exist for quantum computing; therefore, it is important to establish some frameworks that can support the responsible use of this technology in drug discovery and healthcare more broadly.

9. Future Directions and Opportunities

Quantum computing has rapidly advanced, promising solutions for computational challenges in drug discovery and healthcare. Despite limitations, ongoing research can enhance quantum computing's size, speed, scalability, and accuracy. Complex healthcare problems can be modeled with quantum technologies, leading to interdisciplinary collaboration among scientists, engineers, and healthcare professionals. Such collaboration can foster innovation and create new applications, including quantum-enhanced telemedicine. By combining quantum computing with AI, high-performing models can be developed for drug discovery, screening vast compound libraries for potential drug targets. Quantum machine learning may identify active compounds among billions and model compound-target interactions to predict activity. Personalized medicine may benefit from quantum computing by screening a patient’s viral genome against new drugs and analyzing drug-binding sites on proteins. Combinatorial Hamiltonians can model drug combinations, evaluating their synergistic effects on viral infection. Quantum computing can refine pharmacophore design by optimizing molecular arrangements to improve drug efficacy. The integration of advancing quantum technologies with drug discovery and biological problems could result in transformative impacts and the creation of new sectors. Investment in quantum computing for healthcare and drug discovery is crucial. The convergence of quantum computing, computer science, chemistry, biology, and medicine can yield fresh approaches to complex healthcare issues. Educated personnel in quantum technologies will be essential to support emerging industries. There is a need for education in quantum computing to produce a workforce capable of addressing drug discovery and healthcare challenges. Training in quantum computing with a focus on pharmaceuticals and biomedicine will greatly assist academic institutions and industries in utilizing these technologies. Strategic investments in education and quantum technologies can enable advancements in medicine, significantly impacting disease prevention, diagnosis, and treatment.

References:

Shams, M., Choudhari, J., Reyes, K., Prentzas, S., Gapizov, A., Shehryar, A., Affaf, M., Grezenko, H., W Gasim, R., Naveed Mohsin, S., Rehman, A., & Rehman, S., 2023. The Quantum-Medical Nexus: Understanding the Impact of Quantum Technologies on Healthcare. ncbi.nlm.nih.gov

F. Flöther, F., 2024. Early quantum computing applications on the path towards precision medicine. [PDF]

Li, W., Yin, Z., Li, X., Ma, D., Yi, S., Zhang, Z., Zou, C., Bu, K., Dai, M., Yue, J., Chen, Y., Zhang, X., & Zhang, S., 2024. A Quantum Computing Pipeline for Real World Drug Discovery: From Algorithm to Quantum Hardware. [PDF]

A. Cordier, B., P. D. Sawaya, N., G. Guerreschi, G., & K. McWeeney, S., 2021. Biology and medicine in the landscape of quantum advantages. [PDF]

F. Flöther, F., 2023. The state of quantum computing applications in health and medicine. [PDF]