As quantum computing companies look for sectors where their technology can have the earliest and largest possible impact, many have been drawn to chemistry, material science, and pharmaceuticals.
This is largely due to the ability of quantum computers to simulate, generate, and/or analyze the complex molecular structures foundational to this region of science, as well as their ability to drive more practical time and financial savings into what otherwise can be lengthy and costly processes. For example, a variety of research has suggested that the development cost for just one approved new drug can reach into the billions (in excess of $2 billion according to a 2023 Deloitte report), a cost passed on to consumers in the form of higher prices at the pharmacy.
That is why companies such as Microsoft Azure and SandboxAQ have bet on areas such as computational chemistry and drug discovery with quantum offerings.
The latest news in this vein comes from Swiss-German start-up Terra Quantum, which claims to have a new method for helping researchers to more quickly understand molecular structure, one of the biggest bottlenecks in the drug discovery process.Terra Quantum, in joint research with Prof. Dr. Christoph Bannwarth of RWTH Aachen University in Germany, was able to leverage quantum-based tensor networks to a 5x to 20x speed-up in predicting molecular structures compared to approaches that leveraged classical computing processes.
The new development is outlined in a research paper titled “Tensor Train Optimization for Conformational Sampling of Organic Molecules.” The new technique improved upon the traditional approach in the analysis and prediction of a wide dataset of molecules, including penicillin and ritonavir (a molecule used in treatment for HIV).
Tensor networks are mathematical structures used in quantum physics to efficiently represent and manipulate complex multi-dimensional data, according to Terra Quantum. These structures can represent complex configurations and operate directly on the mathematical properties of the molecules without the need to be trained on pre-existing data, which is typically required when using conventional AI methods. Using tensor networks could improve upon a computational process known as conformer search that currently is used to discover molecules with the most stable 3D structure. Because the number of possible conformations increases exponentially with respect to the number of flexible bonds of a molecule, computing them can take too much time and be cost-prohibitive, Terra Quantum said.
Dr. Bannwarth added, "With the tensor train-based optimization, conformer sampling of molecules can be significantly accelerated, compared to the state-of-the-art approaches in the field. This opens up opportunities to treat large molecules that would otherwise lead to an explosion in the computational costs. Hence, we now added an important tool for performing predictive quantum chemical investigations on large molecules."
Terra Quantum Founder and CEO Markus Pflitsch said, “Being able to explore a wider chemical space could allow for significant acceleration in the drug design process by reducing the time needed to find a drug candidate most likely to be successful.”
The next phase of this research will focus on larger molecules, cyclic peptides, calculating protein binding affinities and working with industry partners to address specific needs, according to Terra Quantum.