Schrödinger's Equation Turns 100: How Quantum Chemistry Is Improving Drug Discovery
By Ray Dogum, Chief Editor, Drug Discovery Online
One hundred years ago, Erwin Schrödinger published a breakthrough discovery that earned him the 1933 Nobel Prize in Physics and would forever change our understanding of nature. Arizona State University professor, Sergei K. Suslov, provides a nice detailed timeline and historical context, for curious readers.
To simplify, his work revealed that every particle is governed by wave-like probability laws rather than their position being deterministic. Schrödinger’s equation is a fundamental equation in quantum mechanics that describes how the quantum state of a physical system changes over time. It allowed physicists to predict electron orbitals in atoms, interference patterns, and the structure of molecules. This reality underpins everything from the behavior of particles at the atomic and subatomic level to the folding of proteins.
Schrödinger’s Perspective Of Biology
In addition to being an illuminating physicist, he tried to answer the question: “How can the events in space and time which take place within the spatial boundary of a living organism be accounted for by physics and chemistry?” This resulted in a book he elegantly titled, What Is Life? The Physical Aspect of the Living Cell. For more background, check out Biophysicist Rob Phillips’s take on the book published in 2025.
On January 26th, as we celebrate this centennial milestone and think about all the exponential technologies that are empowering scientists and drug developers around the world, it’s worth asking: What does quantum mechanics mean for drug discovery and biomolecular simulations?
Designing Novel Drugs With Quantum Mechanical Simulations
Drug chemistry is extraordinarily complex. It’s estimated that there are 1060 realistic drug-like molecules that could ever be synthesized. As Loong Wang, CEO of QDX and CTO of Automera, put it, “All of computational chemistry is, in some sense, downstream of quantum mechanics. Because at the end of the day, computational chemistry is about answering questions around how atoms move.” This insight is profound. Every molecular interaction, every binding event, every chemical reaction in the body is governed by quantum laws.
Yet, simulating these phenomena at scale remains a challenge. “Doing quantum mechanical simulations is a very deep complement to experimentalism and is only not done because of how expensive it is,” Wang explained. That’s why approximations (classical simulations and AI models, which are really extrapolations) dominate today’s biomolecular prediction workflows.
But approximations have limits. “If you want the best and most accurate model of how that’s going to happen, you want a quantum mechanical model,” Wang said. This level of accuracy matters when designing drugs for complex targets, such as the JAK2V617F mutation, a key driver of myeloproliferative neoplasms (MPNs). In patients with MPNs, this mutation leads to uncontrolled production of certain blood cell types, resulting in a range of serious complications, including heart attack, stroke, anemia, and leukemia. QDX helped Prelude Therapeutics discover a novel selectivity mechanism by mapping the differences between mutant and wild-type JAK2 proteins.
Scaling Quantum With Modern Computing Technologies
“The physics has been established for 100 years,” Wang noted, but what has changed is our ability to compute. Advances in parallel hardware and algorithmic innovation have made large-scale quantum simulations commercially viable. “We had to go back to the recipe and say, is there a different way of approaching this? Can we do things in a different order?” These optimizations have led to thousands-fold performance improvements in quantum chemistry software, enabling simulations that were once unimaginable.
Does Quantum Chemistry Rely On Quantum Computers?
“No,” Wang says. “Quantum chemistry takes advantage of [CPU-GPU] computing to try and predict quantum phenomena. And in fact, quantum chemistry simulations of the likes that we can do, can actually be very helpful for trying to build quantum computers, and help figure out how to capture those quantum phenomena and how to design them in order to do compute. And then likewise, we can use the results of quantum computing to help improve the accuracy of our own simulations.”
Discovery In A State Of Superposition
As we look ahead, the integration of quantum-native benchmarks into initiatives like the international scientific competition Critical Assessment of protein Structure Prediction (CASP) could help bridge the gap between classical and quantum approaches. Unfortunately, due to NIH cuts, CASP may not occur this year but that doesn’t seem to be slowing down progress in biomolecular prediction models.
It's difficult to predict what the future holds for the drug discovery industry. Perhaps, in the next century, quantum computing will make these simulations routine, unlocking new frontiers in medicine, material science, and beyond.