Quantum theory tries to explain the nature and behavior of energy on the atomic and subatomic level. Computing uses computer technology to develop algorithms that are tasked with solving specific tasks.
How are these two seemingly separate fields related?
Quantum computing combines computers and quantum theory to help scientists process complex tasks much faster and with higher accuracy than traditional computers. Indeed, quantum computers can process a high number of bits – the smallest units of data that a computer can process – that are in different states at the same time.
“Supercomputers” with radically enhanced processing capabilities.
Indeed, quantum computers can process highly realistic simulations involving millions of interconnected and evolving inputs – such as the reaction of the human body to drugs, medicine, and therapeutics.
In fact, quantum computing is being applied with great success in the field of life sciences – the study of living organisms and life processes. A study conducted by Pistoia Alliance found that 82% of life science organizations agreed that quantum computing will have a commercial impact within the next decade.
So why is Quantum Computing such a big deal? What are examples of its real-world use cases? Can we expect quantum computing to contribute to the betterment of healthcare processes for organizations and patients alike?
In this article, we will present 3 ways in which quantum computing can revolutionize life sciences.
1. Precision Medicine Therapies
The first way that quantum computing can revolutionize life sciences by successfully linking genomes and outcomes. This will facilitate the development of precision medicine therapies.
What does this mean?
Thanks to 30 years of research, scientists have successfully mapped the human genome, and are now able to understand primary sequences. Their attention has now shifted to fathoming how genomic sequences translate to function.
In layman’s terms, this simply means understanding what specific genomes actually do.
Unfortunately, understanding genome function is no easy task. Indeed, scientists must somehow make sense of the 3 billion DNA base pairs that exist across human populations. The sheer number of possibilities and outcomes is beyond the capabilities of current computing technology.
Scientists believe that have almost mapped the entire human genome. The next step is figuring out how the genome links to function.
Thankfully, quantum computing can help scientists accelerate fundamental genome to function discoveries in three main ways:
- Motif discovery and prediction: quantum computing’s powerful algorithms can accelerate the identification of important patterns that activate or inhibit gene expression.
- Genome-wide association studies (GWAS): this process tries to establish a link between the single DNA mutations and the expression of diseases. Current algorithms developed on ‘normal’ computers are high-dimensional and computationally challenging. Quantum computing has the potential to narrow the list of candidate genes and hone in on the specific candidates that need to be closely examined This will save organizations both time and money, while accelerating the development of life saving drugs.
GWA is a process by which genomes are scanned to find genetic variations associated with specific diseases.
- De-novo structure prediction: scientists have an abundance of sequencing information and technology. What they lack is deep and intricate understanding of how various structures translate into actual function. Quantum computing can improve the structure prediction for RA molecules, DNA-protein complexes, and other constructs.
All of this may seem highly technical and fairly abstract. Here is a real-world example of how quantum computing can link the relationship between genomes and diseases.
Imagine this scene: you are in the hospital for a particular ailment, which turns out to be a serious disease. Your doctor runs your DNA in a quantum computer, analyzes the results and says: “Based on your genetic makeup, we can confidently say that our treatment will produce this specific result”.
At this stage, medical uncertainty will be a distant memory.
2. Improving Patient Outcomes
Developing predictive therapies will improve patient outcomes. In a perfect world, they will result in medical treatments with a 100% rate of success. Indeed, how can such treatments fail if they are tailored to your individual genome?
This sounds simple, but the human body has millions of variables that interact not only with each other, but also with external inputs. It is very difficult to predict how a foreign substance – in this case, a drug or treatment – will impact different bodies. There are so many factors at play that the same drug can produce very different reactions in people with almost the same genetic makeup.
Thus, it is vital that treatment being developed do not produce negative effects due to minor molecular oversights. This is an extremely important aspect of drug development – one which quantum computing can solve.
Typically, drug discovery involves screening 200,000 to > 106 compounds in experimental and computational workflows. Ultimately, only a few thousand are produced and tested in the necessary battery of assays.
The role of amino acids is well known, but the specific role of each protein, and how they combine to produce specific reactions, is yet to be fully understood.
Quantum computing could help scientists assess more candidate molecules and evaluate their characteristics and impacts more accurately. This is a very important advance because the number of small molecules is enormous.
Current computational limits mean that only a fraction of molecules are considered during the discovery process: indeed, while up to 106 of compounds are screened, it is estimated that the total possible carbon-based compounds whose molecular masses are similar to those of living systems is 1060 or more.
Quantum computer would be a major step towards running comprehensive and realistic simulations.
3. Developing Novel Biological Products
The third way that quantum computing can revolutionize life sciences is by helping scientists develop biological drugs based on protein folding predictions.
Biological drugs use a protein or another macromolecule to fight a given disease. For example, antibodies and insulin, two notable biological drugs, have been used with great success for decades. With the advent of quantum computing, these types of drugs will become easier to develop and market.
Until now, researchers were overwhelmed by the sheer number of potential conformations and chain lengths. Consider this: in a single model, a chain of twenty amino acids can have 109 potential conformations, and chains with 60 and 100 amino acids can have up to 1028 and 1047 potential conformations.
Biological drugs are based on living cell cultures, which makes them highly efficient for treating complex diseases.
Further, the FDA requires that proteins studied during biological drug development have more than 40 amino acids. Obviously, this pushes current computational power to its limits.
Thankfully, quantum computing has the ability to score the vast number of possible structures and identify the ones likeliest to achieve the desired results. In addition, quantum computing can significantly improve the calculation of protein force fields.
Ultimately, this will help researchers discover proteins that can treat and cure diseases that currently rely on legacy treatments with questionable efficiency.
The Bottom Line
Quantum computing has the potential to radically improve healthcare processes and deliverables:
- On the one hand, drug companies will be able to perform high-precision research, which will help them minimize costs and increase the likelihood of commercializing new drugs, therapies and treatments.
- On the other hand, patients will benefit from individualized treatments that will produce higher success rates and, ultimately, increase longevity.
Despite promising initial results, quantum computing is still in its early innings, and billions of dollars are being invested in order to accelerate its development over the coming years.
By: Michael Megarit