For those who are familiar with the realm of high-powered computing, the promise of quantum computing has often appeared tantalizingly close for many years. It offers the potential for lightning-fast solutions capable of performing calculations that are beyond the reach of classical computing systems, including the cloud architectures that have evolved over the last decade. Many academics and industry observers have noted the potential of quantum optimizers as a bridge technology to the fully fault-tolerant quantum computing era, recognizing the utility and scalability of this quantum technology available today. This technology is being used not only in drug discovery but in the world of fintech and logistics to optimize portfolio management and shipping systems to solve real-world problems today.
Much of the quantum computing effort in the pharmaceutical industry focuses on molecular modeling. The Boston Consulting Group predicts that by 2030, pharmaceutical companies will be able to model systems containing up to 100 atoms. This capability would enable the modeling of small-molecule drugs, such as ibuprofen, in isolation. However, modeling these drugs within biological environments or modeling peptides like Ozempic would require systems with up to five times more atoms. This represents a tremendous potential for understanding molecular interactions that lie at the root of many diseases in a way that we have never been able to, but will require much larger fault-tolerant quantum computing architectures than what is available today or in the next 5 years. In the meantime, there exists a “bridge technology” that provides quantum utility, enabling researchers to tackle complex problems formulated as multi-variable optimization problems.
At PolarisQB, we leverage the capabilities of quantum computers, including the Advantage system from the Canadian quantum computing leader D-Wave, to optimize drug design by transforming our chemistry challenges into combinatorial optimization problems. This allows us to search a chemical space of up to a nonillion molecules (10^30) to find optimal molecular solutions that fit a variety of drug-like characteristics in a matter of hours or days rather than months or even years. By bringing today’s available quantum computing technology to bear in this way, we are accelerating the process of computational chemistry for drug discovery today and building a platform whose power will increase exponentially as the power of quantum computers continues to grow.
