Exploring quantum computing use cases for life sciences

No doubt you’ve heard it before. Quantum computers can do some things more efficiently than classical computers. Is that a big deal? Why are advanced computational approaches even needed?

In life sciences, major challenges include understanding the relationships among sequence, structure, and function and how biopolymers interact with one another as well as with small organic molecules that are native to the body or designed as drugs. Such problems are computationally complex and are at the heart of genomic analysis, drug design, and protein folding predictions.

Consider drug design. The number of molecules made up of say 50 atoms that can be built using just 10 different types of atoms amounts to around 10⁵⁰. If, in addition, we also add the exponentially large number of possible molecular configurations and conformations that can be sampled at room temperature, the total number of molecules that could potentially constitute a valid drug is larger than the roughly 10⁸⁰ atoms in the observable universe. Tackling this level of complexity is far beyond the capabilities of classical computers; however, quantum computers could make inroads.

The famous physicist, Richard Feynman, suggested back in the 1980s that “if you want to make a simulation of nature, you’d better make it quantum mechanical.”  So, what we’re talking about with quantum computing isn’t merely speed. It’s about tackling problems differently and making the seemingly impossible possible, if not commonplace.

As a result, there is now a cross-industry race toward quantum applications. Within five years, it is possible quantum computing will be used extensively by new categories of professionals and developers to solve problems once considered unsolvable. In the life sciences industry, quantum computing is expected to enable a range of disruptive use cases. These include:

1. Creating precision medicine therapies by linking genomes and outcomes

2. Improving patient outcomes through enhancing the efficiency of small-molecule drug discovery

3. Developing novel biological products based on protein folding predictions