Enko Named “AI-Based AgTech Platform of the Year” in 2026 AgTech Breakthrough Awards
Enko Chem, Inc. has announced that its artificial intelligence-powered discovery platform, ENKOMPASS™, has been named “AI-based AgTech Platform of the Year” in the 7th annual AgTech Breakthrough Awards program. The recognition highlights Enko’s approach to transforming crop protection discovery through artificial intelligence, advanced chemistry, DNA-encoded library screening and machine learning.
The award recognizes technology designed to address one of agriculture’s most persistent challenges: discovering new crop protection chemistry quickly and efficiently while reducing the substantial time, cost and risk traditionally associated with early-stage research and development.
For Enko, the recognition reflects its broader goal of changing how new crop protection solutions are discovered and developed. ENKOMPASS is designed to help scientists search through vast chemical possibilities, identify promising candidates earlier and make more informed decisions about which compounds should advance through development.
“Faster progress. Lower cost. Greater impact. I am proud to see the Enko team recognized for what we set out to do, which is change the speed and economics of crop protection discovery,” said Tony Klemm, CEO of Enko. “ENKOMPASS gives us a fundamentally different way to search for and advance new chemistry at a time when agriculture needs new solutions faster than ever.”
Addressing the Challenges of Crop Protection Discovery
The discovery of new crop protection chemistry is traditionally a complex, expensive and highly uncertain process. Researchers can spend significant amounts of time and capital designing, synthesizing and testing individual compounds, even though most of the molecules investigated will ultimately fail to become commercial products.
This creates a major challenge for agricultural innovation. While growers need new tools to manage evolving pest pressures and resistance, conventional discovery programs can require years of research before scientists know whether a particular chemical candidate has the potential to succeed.
ENKOMPASS is designed to address this challenge at the earliest stage of discovery. By combining DNA-encoded library (DEL) screening, machine learning and advanced molecular design, the platform enables Enko scientists to evaluate enormous chemical spaces and identify molecules that warrant further investigation.
Rather than relying solely on sequential testing of individual compounds, Enko’s technology is designed to generate insights from large-scale molecular datasets. The approach can help researchers prioritize promising candidates earlier, potentially reducing the amount of time and resources spent on chemistry that is unlikely to progress.
Using Data to Improve Discovery
A central element of ENKOMPASS is its ability to incorporate new information into the discovery process. As Enko generates additional experimental data, its machine learning models can use that information to improve predictions about which molecules are most likely to demonstrate desirable characteristics.
The process creates a continuous learning cycle. Screening generates data, the data informs computational models, and those models help guide subsequent discovery and experimentation. Scientists remain central to the process by interpreting results, validating predictions and determining which candidates should move forward.
This combination of computational capabilities and scientific expertise is intended to accelerate learning throughout early-stage research. Instead of waiting until later stages to determine whether a candidate has potential, researchers can use data-driven insights to make earlier decisions and focus resources on the most promising chemistry.
The approach is particularly important in crop protection, where the development process must ultimately address multiple scientific, environmental, regulatory and commercial requirements.
Identifying Potential Challenges Earlier
Discovering a molecule that demonstrates activity against a target is only one step toward creating a viable crop protection product. New chemistry must also meet regulatory requirements, demonstrate an acceptable environmental and safety profile and have the potential to be manufactured at commercial scale.
Traditional discovery programs can sometimes uncover these challenges after substantial resources have already been invested. Enko aims to address that issue by integrating multiple elements of discovery into a more connected process.
ENKOMPASS brings molecular design, greenhouse testing and early safety screening together as part of its discovery strategy. This allows researchers to investigate important characteristics earlier and identify potential development obstacles before they become significantly more expensive to address.
The result is intended to be a more efficient pathway from initial molecular discovery toward viable crop protection candidates. By prioritizing chemistry with a clearer development path, Enko seeks to improve the economics of research while accelerating the arrival of new solutions for agriculture.
AI Meets Advanced Chemistry
The recognition from the AgTech Breakthrough Awards reflects the growing role of artificial intelligence and data-driven technologies in agricultural research. AI can analyze large datasets and identify patterns that may be difficult to uncover through conventional approaches alone, while advanced chemistry provides the scientific foundation for designing and evaluating new molecules.
ENKOMPASS combines these capabilities within a crop protection discovery environment. Its use of DEL screening allows researchers to explore extensive molecular libraries, while machine learning helps interpret the resulting data and guide subsequent discovery efforts.
The objective is not simply to generate more chemical candidates, but to improve the process of determining which candidates deserve additional investment.
Bryan Vaughn, Managing Director of AgTech Breakthrough, said agriculture needs new chemistry and needs it faster than the traditional discovery model can deliver. He added that ENKOMPASS changes the economics of discovery by enabling Enko to evaluate billions of molecules and concentrate resources on the strongest candidates earlier.
“We’re pleased to recognize ENKOMPASS as ‘AI-based AgTech Platform of the Year,’” Vaughn said.
Responding to a Changing Agricultural Landscape
The need for innovative crop protection solutions is becoming increasingly important as growers face evolving pest pressures and the spread of resistance. At the same time, regulatory expectations surrounding agricultural chemicals continue to increase, adding complexity to the development of new products.
These pressures can make conventional discovery approaches increasingly challenging. Companies must identify new modes of action and effective chemistry while ensuring that potential products can meet stringent safety, environmental and manufacturing requirements.
Enko believes a technology-driven discovery model can help address these challenges by moving critical decisions earlier in the research process.
ENKOMPASS brings together artificial intelligence, advanced molecular science and scientific expertise to help identify and advance new crop protection candidates. By analyzing larger chemical spaces and learning from accumulated data, the platform is designed to help scientists work more efficiently while reducing the risks associated with early-stage discovery.
The AgTech Breakthrough recognition represents another acknowledgment of the role that AI and computational technologies can play in agricultural innovation. For Enko, the award reinforces its mission to accelerate crop protection discovery and help create a more efficient path toward the next generation of agricultural chemistry.
As growers continue to face resistance, changing pest populations and increasing demands for sustainable crop protection, technologies capable of speeding up discovery could become increasingly important. Through ENKOMPASS, Enko is seeking to make the process faster, more data-driven and more economically efficient—ultimately helping bring new crop protection solutions from discovery toward the field.
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