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Highlights
- Research on small and large molecule properties and their interactions has always formed the basis of rational drug discovery. Quantum-inspired algorithms are expected to predict biochemical reactions better, as atomic, and sub-atomic particles follow quantum principles.
- Minimizing damages to surrounding healthy tissues and organs during radiotherapy is a highly complex optimization problem with thousands of variables. Quantum computers have the potential to identify the most optimized and precise radiation and therapy plans after comparing all possible approaches.
- Most compute-intense problems fall into three major categories—artificial intelligence (AI) and machine learning (ML), simulation, and optimization—where applying quantum computing returns immense value.
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Disruptive power of quantum
Quantum computing to accelerate pharma research
The disruptive powers of quantum computing will drive and reshape the life sciences industry, especially in the areas of drug discovery and development. The laws of quantum mechanics have enormous potential in terms of speed and in solving complex problems that were unsolvable till now. To that end, the life sciences industry is focusing on identifying appropriate use cases for quantum computing.
This paper discusses some of the key life sciences applications that quantum computing can revolutionize and discusses the way forward.
Applying quantum computing in life sciences
In traditional computing, bits denote 0 or 1, whereas quantum bits or qubits in quantum computing can denote both 0 and 1 at the same time. While bits function independently, qubits interact with one another leading to exponential growth of states. That essentially means that quantum computers are not restricted to perform stepwise calculations but can compute a vast number of calculations simultaneously at a speed unimaginable with classic supercomputers.
Given the incredible potential for innovation, quantum computing is expected to significantly disrupt the pharma and life sciences value chain (see Figure 1). Conventionally, research on small and large molecule properties and their interactions form the basis of rational drug discovery. Quantum-inspired algorithms are expected to predict biochemical reactions better, as atomic and sub-atomic particles follow quantum principles.
Figure 1: Quantum computing disrupts the pharma and life sciences value chain
Most compute-intense problems fall into three major categories – artificial intelligence (AI) and machine learning (ML), simulation, and optimization – where applying quantum computing returns immense value.
Apart from this, there is research focus on quantum technologies in imaging and cryptographic keys. Here we explore the potential use cases of quantum computing in these five areas of the life sciences industry.
Simulating drug interactions
Simulating chemical reactions and interactions at molecular and sub-molecular levels is currently based on several approximations and assumptions due to compute infrastructure limitations. Industry segments like chemicals, materials, and pharmaceuticals, expect quantum developments to enable better predictive models of chemical or molecular synthesis and reactions based on simulations at molecular, atomic, and sub-atomic levels. Quantum computers can handle problems in estimating reaction rates and mechanisms better than classical computers.
Humans have more than 20,000 genes, which are responsible for coding nearly 20,000 proteins. Further, each protein has nearly 100-200 variants. The Protein Data Bank currently has up to 150,000 protein structures, of which around 46,000 are human proteins. Any chemical used as a drug can potentially bind to one or more of these 20,000 proteins, causing either beneficial or adverse reactions.
The multiple molecular interactions between protein-protein, enzyme-substrate, protein-nucleic acid, drug-protein, and drug-nucleic acid play important roles in biological processes like signal transduction, transport, cell regulation, gene expression control, enzyme inhibition, and antibody-antigen recognition. Besides human proteins, there are proteins on pathogens and commensal microbes with which drugs can interact in human bodies. Quantum computers of the future are expected to accurately simulate these interactions.
While today’s quantum computers can simulate small molecules like hydrogen, lithium hydride, and beryllium hydride, this is a fairly smaller achievement in terms of molecule size and structural complexity. However, considering today’s low-scale quantum computers, this is quite an accomplishment.
Addressing optimization problems
Quantum computing is expected to solve the classic traveling salesman problem (TSP) faster and with higher accuracy. Although the salesman would not have to visit thousands of cities, this problem is relevant for solving several optimization issues across travel and transportation, supply chain, network infrastructure, air traffic control, work scheduling, financial services, and protein folding. Today’s computers find it difficult to understand how real proteins fold into the shapes that help give them their function.
In 2012, a Harvard research team reported the usage of quantum annealing to solve protein folding problems. Quantum computing can address other optimization problems like molecular recognition, protein design, and sequence alignment. Researchers at the University of Southern California recently demonstrated the use of quantum processors to predict the binding of gene regulatory proteins to DNA.
The healthcare industry also faces operational-level optimization challenges like scheduling healthcare providers and deciding therapy regimens for cancer and other diseases. Minimizing damages to surrounding healthy tissues and organs during radiotherapy is a highly complex optimization problem with thousands of variables. Quantum computers have the potential to identify the most optimized and precise radiation and therapy plans after comparing all possible approaches.
ML and quantum computing
Running deep neural networks on huge datasets is constrained by the number of layers that current computing infrastructures can manage. Exponential scaling in compute power can support complex model building steps and allow real-time model building. With no lag in model building, AI systems can continually learn in real time, as new data is generated. Pharma research and development organizations are exploring AI and ML to understand disease mechanisms, identify biomarkers and targets, and predict compound properties, activities, and adverse reactions. It can also be used for de novo design and synthesis of small molecules, clinical trial analytics, image analytics for better diagnostics, and in analyzing literature, documents, and patents. Quantum computing can solve challenges like managing large datasets generated by high throughput systems, running compute-intensive analytics, and building effective ML models.
Quantum cryptography
The current data encryption techniques are based on algorithms that are mathematically impossible to break by classic computers. However, quantum computers are expected to break into these encryptions easily with an exponential increase in compute power. As quantum computing can secure the key and data indefinitely with guaranteed unbreakable encryption, the National Institute of Standards and Technology (NIST) focuses on providing quantum-based encryption techniques for the future. The possibilities of applying quantum mechanics principles for developing un-hackable encryptions based on quantum key distribution is also underway.
Data is sacrosanct in the life sciences industry, from the intellectual property (IP) perspective and for ensuring patient data privacy. The definition of personal identifiable information (PII) is also evolving along with the new omics technologies. Protected access to medical records and secure data-sharing using quantum-based encryptions could be some of the earliest implementations of quantum computing in life sciences.
Future Technology
Transforming how we diagnose
The image capturing techniques for high-resolution images, CT-Scan, MRI, or HD videos generate large-sized files. Traditional image analysis relies on representing images pixel-by-pixel, needing enormous compute resources for high-resolution images. Quantum computing has the potential to transform imaging and image processing through faster results. The potential to store N bits of classical information in log (2N) qubits will enable more granular-level imaging and image analysis that is unimaginable today. Image analysis of tissues, cells, and sub-cellular levels will transform how we diagnose, treat, and monitor diseases.
Case Western Reserve University (CWRU) and Siemens Healthcare had collaboratively developed an approach for data acquisition, post-processing, and visualization, termed magnetic resonance fingerprinting (MRF), in 2016. This permits the simultaneous, non-invasive quantification of multiple important properties of a material or tissue and can revolutionize MRI-scan-based medical imaging with accuracy, speed, and predictive capacity. CWRU also collaborated with Microsoft to bring this to clinical reality with the help of quantum computing.
The technology of the future
Although the basic commercial quantum computing infrastructure is already available, we are nearing the large-scale operationalizing phase with accelerated innovations in hardware. Many big pharma companies have announced collaboration with quantum hardware or software players to evaluate proofs of concepts. A consortium, named QuPharm has also been formed to work on identifying use cases and plan for precompetitive research in collaboration with Pistoia Alliance and Quantum Economic Development Consortium (QED-C).
Identifying best-fit use cases and employing resources with quantum computing knowledge and skills are top priorities now. The more we explore the field, the more we realize the complexities involved. Many of these complexities will unravel themselves with quantum-based advancements in computing and storage. Quantum computing could be the technology of the future that will enable accurate in silico de novo designing of efficacious and safe drugs.
