In 2022, the drug discovery market was valued at $55.46 billion, and by 2032 that figure is expected to more than double to $133.11 billion. This is a huge sum of money, but after events like COVID-19, we now know that time is equally of the essence in the drug discovery industry.
With the discovery of new drugs taking up to ten years and the cost of development rising, quantum computing could have a significant impact in this area. In terms of quantum use cases, the development of broad-spectrum antiviral medications will provide immediate benefits. The pharmaceutical industry is becoming increasingly digital, with the aim of optimizing and maintaining processes throughout the development lifecycle to save time and resource costs, in line with the advent of new computing technologies.
What are the realistic quantum benefits for the life sciences?
First, the discovery of drug compounds with quantum computers will take noticeably less time than on a classic business computer, compressing the research to generate quality leads from years to a few months or weeks.
A core activity of pharmaceutical companies is the development of therapies that will treat or cure challenging diseases. Classical computers have limited computing power, and predicting accurate molecular behavior can take years. Quantum computing can significantly reduce early drug discovery and optimize the development cycle, significantly shortening the time to clinic.
Quantum computing is ideally suited to optimize this process, with the biggest benefit of quantum computing being an increase in the accuracy of calculations beyond that of any classical computer. This means that the quality of computer-designed connections will improve.
Furthermore, in 2019, pharmaceutical companies spent more than 15% of their revenue on R&D, with some spending more than 20%. The R&D process involves identifying specific molecules that need to be optimized, screening thousands of molecules, and then testing them under controlled conditions, which can take years. It is therefore of utmost importance that the input molecules are of better quality.
Head of Strategic Alliances at Kvantify.
Drug discovery involves multiple phases, and the time and costs vary by phase, but the average cost of bringing a new drug to market is $1.3 billion. The potential for cost savings is enormous. But despite all this, only 10% of drugs ultimately make it through the testing phase.
Quantum will be able to significantly boost research and development in drug discovery, but optimizing clinical trials and minimizing the risk of costly failures will also bring benefits. During drug development, pharmaceutical companies sometimes adopt a trial-and-error approach because the speed at which this is done outweighs the cost of waiting for today’s classical computer calculations. Quantum computers can generate predictive data much faster and more accurately, reducing time, eliminating guesswork and therefore reducing costs.
What are the barriers?
However, even though quantum computers are on the rise, there are still a number of barriers that prevent companies from adopting quantum computers.
Firstly, integrating quantum computing with the existing IT infrastructure is a complex task. Despite being more advanced, quantum computers are developed separately from classical computers, making integration difficult.
Secondly, there is the lack of talent. Many companies do not have the expertise to integrate the technology into their workflows, making the adoption of quantum systems much more difficult. Talent is limited in the quantum space and the supply chain is too narrow to meet demand. Once talent is on board, it can take years to understand quantum technology within the company, making early adoption much more important.
Finally, quantum computing is currently in the development phase and quantum hardware is subject to noise and errors, making algorithms unwieldy for current and near-future devices. The development of new methods and algorithms that take into account current noise and errors will help reduce measurement overheads.
When to invest in quantum computing?
Quantum computing is still in its early stages and there are barriers and hurdles to business use, but that hasn’t stopped companies from investing. Quantum is expected to generate operating income of up to $850 billion by 2050, and will be a major force in drug development, financial market pricing, and AI and ML.
The life science sector is one of the sectors likely to see an early quantum impact and record investments. While attracting and training talent may take years, the long-term benefits will only increase as top industries potentially gain $1.3 trillion in value by 2035.
Taking this into account, it is important that companies invest in quantum as early as possible. Overcoming barriers such as recruiting relevant talent and integrating systems takes time. By taking action now, early adopters will get a head start in tackling these complex problems.
Furthermore, quantum research is constantly evolving and showing many long-term benefits in the life science sector. For example, quantum algorithms such as Kvantify’s FAST-VQE, which is designed to perform complex chemistry and find the energy in a chemical system, are already being developed and hold great promise for the future.
Overall, it is important that companies start investing in quantum readiness now so that we do not experience a gap between solution readiness and companies not being able to benefit. Additionally, it’s worth noting that there are quantum companies that put business first and can help companies that don’t have the resources or talent.
Early investments in talent and infrastructure will yield significant returns, including revenue gains/savings and time savings, as quantum computing matures, widening the gap between quantum adopters and classical computing companies. As investment increases, especially in the life sciences and drug development sectors, it is becoming more important than ever for companies to invest to avoid falling behind the competition, or even to get ahead.
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