COVID-Era Group Testing Could Transform Drug and Chemical Discovery
Laboratories around the world were forced to get creative during the early days of the COVID-19 pandemic. One of the most effective workarounds emerged from necessity: when testing kits were scarce, labs pooled samples from multiple patients and ran a single test. If the pooled test came back negative, everyone in that group was cleared. If it came back positive, follow-up tests identified the infected individuals. This clever strategy, known as group testing, saved time, money, and precious resources.
Now, that same idea has found a surprising new home in chemistry, where it may dramatically speed up the discovery of drugs and other valuable chemicals.
A research team led by Eric Jacobsen, the Sheldon Emery Professor of Chemistry at Harvard University, in collaboration with scientists at Merck, has adapted group testing principles to tackle one of chemistry’s most stubborn problems: identifying cooperative catalysts efficiently. Their work was published in Nature under the title Accelerating the discovery of multicatalytic cooperativity.
How Group Testing Found Its Way Into Chemistry
Catalysts are substances that speed up chemical reactions or make them more efficient without being consumed. Chemists have long known that sometimes two catalysts working together can outperform either one on its own. These cooperative effects can lead to higher yields, greater selectivity, or milder reaction conditions, all of which are highly desirable in industrial and pharmaceutical chemistry.
The problem is scale. Even a relatively small collection of catalysts quickly becomes overwhelming to test.
For example, a set of 50 potential catalysts contains more than 1,200 unique pairs. Once chemists start thinking about three-way or four-way combinations, the number of possible experiments becomes essentially impossible to manage using traditional methods.
This is where the COVID-era group testing mindset came into play.
The Core Idea: Pooling Instead of Pairing
Instead of testing each catalyst pair individually, the researchers designed pooled experiments. In these experiments, each reaction flask contained multiple catalyst candidates arranged in a specific mathematical pattern.
A custom-built statistical and computational algorithm then analyzed how each pool performed. By comparing results across many pools, the algorithm could infer which specific catalyst combinations were responsible for improved—or reduced—reaction performance.
The goal was similar to COVID testing: identify the best performers using the fewest possible tests.
Importantly, the algorithm does not require detailed prior knowledge of the chemical properties of each catalyst. It relies instead on simple math, statistics, and carefully designed pooling strategies, making it broadly applicable across many chemical systems.
Why You Can’t Just Mix Everything Together
At first glance, one might wonder why chemists don’t simply throw all catalysts into a single reaction and see what happens. The researchers addressed this directly.
Chemical systems are messy and competitive. While some catalysts cooperate, others actively inhibit each other. Dumping everything into one flask almost guarantees failure, producing what the researchers describe as “mud,” where positive effects cancel out and nothing works well.
The pooling strategy avoids this problem by limiting how many catalysts appear together in each experiment and by carefully controlling which combinations occur across different pools.
Handling the Complexity of Real Chemistry
One major challenge in adapting group testing from medicine to chemistry is that chemical reactions are not binary. COVID tests are either positive or negative. Chemical reactions, on the other hand, exist on a spectrum.
A catalyst might slightly improve a reaction in one context and strongly inhibit it in another. Some combinations produce modest gains, others dramatic improvements, and some do nothing at all.
To deal with this complexity, the researchers developed quantitative performance metrics rather than yes-or-no outcomes. Their algorithm accounts for both cooperative and inhibitory interactions, allowing it to distinguish genuine synergistic effects from misleading noise.
Testing the Method in the Real World
Before applying the approach in the lab, the team tested their algorithm on simulated datasets. These simulations showed that the method could reliably identify true cooperative catalyst pairs while ignoring false signals.
With confidence in the framework, the researchers moved to a real chemical challenge proposed by Richard Liu, an assistant professor of chemistry and chemical biology at Harvard and a co-author on the study.
The test case was a palladium-catalyzed decarbonylative cross-coupling reaction, an important transformation used to build complex organic molecules, including many potential drug candidates.
Using their pooling–deconvolution strategy, the team screened numerous ligand combinations efficiently and identified several catalyst pairs that significantly outperformed individual catalysts used alone.
Why This Matters for Drug and Chemical Manufacturing
Catalysts often rely on precious metals like palladium, which are expensive and environmentally taxing to mine. Any method that reduces catalyst loading while maintaining or improving performance is a major win.
The newly identified cooperative catalyst pairs:
- Delivered better reaction efficiency
- Worked under milder conditions
- Required less catalyst material
These advantages translate directly into lower costs, reduced energy consumption, and more sustainable chemical processes.
However, the researchers emphasize that the true value of their framework lies beyond any single reaction. The method offers a general strategy for exploring cooperative effects across many areas of chemistry.
How This Complements Traditional Chemical Design
Chemistry often relies on rational design, where scientists use mechanistic understanding to predict how catalysts should behave. While powerful, that approach can miss unexpected interactions.
The group testing framework acts as a complementary discovery tool, capable of uncovering surprising cooperative effects that theory alone might not predict. Once identified, these combinations can then be studied in detail using traditional chemical analysis.
Looking Ahead: Beyond Catalyst Pairs
The researchers are already thinking bigger. Their long-term vision includes extending the approach to:
- Ternary systems involving three catalysts
- Higher-order cooperativity with even more components
- Integration with high-throughput experimentation
- Potential future links with machine learning models
As computational power and experimental automation continue to improve, strategies like this could open up entirely new regions of chemical discovery.
A Broader Perspective on Group Testing
Group testing has a surprisingly rich history. Originally proposed during World War II to efficiently screen soldiers for syphilis, it resurfaced during COVID-19 and is now influencing chemistry.
This cross-pollination of ideas highlights how solutions developed under crisis conditions can spark lasting innovation in entirely different fields.
In this case, a public health workaround may help accelerate the discovery of better medicines, cleaner industrial processes, and more efficient chemical reactions for years to come.
Research Paper Reference:
Accelerating the discovery of multicatalytic cooperativity – Nature (2025)
https://www.nature.com/articles/s41586-025-09813-2
