A new generation of quantum chemistry software is promising to compress months of molecular simulations into minutes, with direct implications for how quickly new drugs and advanced materials reach the lab bench. By pairing quantum physics with aggressive algorithmic optimization, these tools aim to turn what used to be a bottleneck in discovery into something closer to a real-time design loop.
The most striking example is an ultrafast engine called EXESS, which its creators say can accelerate some electronic structure calculations by several thousand times. If the performance holds up in industry settings, the shift could help drug designers and materials scientists move from trial-and-error screening toward precision engineering of molecules that behave exactly as needed.
How EXESS shrinks month-long calculations to minutes
The EXESS engine is described as operating roughly 3,000 to 4,000 times faster than conventional quantum chemistry codes for certain workloads, a jump that changes what kinds of problems can be tackled on practical timelines. Instead of waiting a month for a high-accuracy simulation of a complex system, the team behind the software reports that some of those jobs can now finish in about 12 minutes, turning a one-off calculation into something that can be iterated many times during a single workday. That speedup is not just a convenience; it effectively opens up whole regions of chemical design space that were previously too expensive to explore in detail.
To achieve that leap, the developers of EXESS focus on rearranging and parallelizing the underlying quantum chemistry calculations so that modern hardware is used far more efficiently. One description explains that EXESS operates 3,000 to 4,000 times faster depending on the type and scale of the computations and that one way the team sped things up was to break large problems into many smaller tasks that can be handled at once, a strategy likened to handing out recipes to multiple cooks in a kitchen rather than asking one person to prepare the entire meal alone, as detailed in an overview of the EXESS performance.
From quantum equations to real drug candidates
Drug discovery has already started to feel the impact of quantum-inspired tools that can sort through vast libraries of molecules more intelligently than brute-force screening. A head-to-head study shared by PolarisQB and amplified in a community post described how drug discovery has hit what the authors call the “Quantum Speedup” era, with a comparison that highlighted how their quantum-assisted workflow could identify promising structures more quickly than a traditional pipeline, a shift that was presented as a turning point for AI in medicine, molecular design, and innovation in general in the Drug discovery has discussion.
Academic groups are moving in the same direction. At Southern Methodist University, researchers built an open-source platform called SmartCADD that combines machine learning with quantum mechanical calculations to speed the virtual fitting of drug candidates into protein targets, likening the challenge to assembling a complex puzzle where each piece must match in three dimensions and in electronic structure, an approach described in detail in a report on SMU SmartCADD. Taken together, these projects show how ultrafast quantum chemistry engines could plug directly into existing AI-heavy discovery stacks, providing more accurate energy and binding calculations without grinding the process to a halt.
Why traditional simulations were holding chemistry back
For decades, chemists have relied on approximate models because exact quantum simulations of real-world molecules were simply too costly on classical computers. Traditional molecular simulations often depend on simplified force fields or coarse-grained models that can miss subtle but important electronic effects, which in turn can lead to incorrect predictions about how a drug will bind to a protein or how a material will behave under stress, as described in an analysis of how Traditional molecular simulations limit drug development.
Quantum simulations promise a way around that by directly modeling the interactions of electrons and nuclei, but until recently these methods were limited to relatively small systems or very short timescales. A technical review of quantum-assisted chemistry notes that quantum simulations are transforming drug development by helping researchers correctly model how proteins and ligands interact, how strongly they bind, and how candidate molecules move through the body, all of which can improve efficacy and safety when the models are accurate, as summarized in a study on Quantum simulations. Engines like EXESS are designed to bring that level of fidelity to much larger and more complex systems by dramatically cutting the computational cost.
New methods for advanced materials and catalysts
The same techniques that help model drug molecules can be applied to materials such as battery electrodes, solar absorbers, and industrial catalysts, where small changes in electronic structure can have large effects on performance. Researchers at the University of Chicago reported a new quantum chemistry method for advanced materials that builds on a framework called the Localized Active Space approach, which was originally developed by a Research Assistant in their group and allows them to focus high-accuracy quantum treatment on the parts of a system that matter most while keeping the rest of the calculation manageable, as explained in their description of the New quantum chemistry.
A follow-up technical note on the same work clarifies that the new method builds on Localized Active Space, often shortened to LAS, and that this strategy helps the team capture the right physics at high accuracy without simulating every electron in the material at the most expensive level, a balance that is essential for studying complex solids and interfaces, as detailed in the discussion of Localized Active Space. Engines like EXESS are likely to draw on similar ideas, using localized treatments and clever partitioning to extend quantum-level modeling to realistic materials that span thousands of atoms.
What faster chemistry means for industry and patients
For pharmaceutical companies, the ability to run high-accuracy simulations in minutes rather than weeks could reshape project timelines and risk profiles. A detailed blog on chemical discovery points out that designing new pharmaceuticals requires screening thousands of molecules and that applications in Chemical Discovery, particularly Drug Development, already depend heavily on computational triage to decide which compounds advance to the lab, as outlined in a review of Applications in Chemical. If an engine like EXESS can inject accurate quantum data into that funnel at industrial scale, it could help companies cut attrition, reduce late-stage failures, and move more targeted therapies toward patients faster.
The developers of EXESS have framed their work in similar terms, noting that there are calculations that would, in principle, take about a month that actually take closer to 12 minutes when run using EXESS and arguing that this kind of acceleration can help make a difference for areas such as new medicines and materials, as described in a profile that quotes the remark that “There are calculations that would, in principle, take about a month that actually take closer to 12 minutes” and highlights how that shift can help make a difference for real-world problems, in a piece focused on More in Science. A related explanation of the same quote emphasizes that this is the first time such a dramatic reduction has been reported for those specific workloads and ties the result directly to the design of EXESS, noting again that “There are calculations that would, in principle, take about a month that actually take closer to 12 minutes” when EXESS is used, as summarized in a separate discussion of There are calculations.
