MIT announced on November 10, 2026 that it will lead a project backed by up to $52.9 million from ARPA-H, the U.S. Advanced Research Projects Agency for Health, to design safer herbicides with generative AI and develop biological systems that break down agricultural chemicals.
Called Precision Learning for On-target Weed control (PLOW), the project belongs to ARPA-H’s Systems for Tracking and Resilience in Efficient Agricultural-input Management (STREAM) program. Christopher Voigt, head of MIT’s Department of Biological Engineering, is principal investigator, with MIT serving as prime awardee. ARPA-H lists September 18, 2026 as the award start date.
Why Weed Control Needs New Chemistry
The project targets a tension in U.S. agriculture. More than 1 billion pounds of herbicide are used each year to prevent significant crop losses and enable regenerative practices that protect soil, but repeated use has allowed some weeds to acquire resistance mutations. Glyphosate illustrates the challenge: it kills weeds by interfering with EPSP synthase, an enzyme plants need to produce several critical amino acids, and resistance makes that mechanism less effective.
Health concerns add another design constraint. Herbicide exposure may contribute to increased risks of cancer, metabolic and endocrine disorders and other adverse outcomes, MIT’s announcement states. Farm workers can be exposed through frequent herbicide use, while many U.S. residents encounter small amounts through food and drinking water. The research therefore includes human and ecological harm among its candidate-selection criteria alongside weed-killing performance.
Supply security adds another objective. The alternatives are intended to reduce U.S. dependence on imported phosphorus, a critical ingredient in glyphosate. Voigt said the combination of resistance and insecure precursor supply chains could leave the country without agrochemicals that are “absolutely essential for getting the high crop yields and economical prices that the United States now sees.”
Generative AI Connected to Experimental Screening
PLOW’s first research group will build models that propose either chemical compounds or small interfering RNA (siRNA) molecules intended to disrupt the plant enzymes targeted by glyphosate and other herbicides. Connor Coley and Regina Barzilay of MIT and Alexander Tropsha of the University of North Carolina lead that work. Their models are intended to optimize candidates for low synthesis cost, low risk of inducing resistance and low risk of harming humans or causing ecological damage, as well as effectiveness.
The team expects the models to generate billions of possible candidates. That scale makes the next stage central: Angela Koehler of MIT and Jaime Cheah of the Broad Institute will lead high-throughput screening to determine which molecules hit their targets. The screening data will then flow back to the computational group to improve its models, creating an iterative design-and-test loop rather than treating AI output as a finished herbicide.
After candidate pools are narrowed, a third group will evaluate each molecule under different agricultural conditions in greenhouse studies. That group will be led by Neal Stewart and Scott Lenaghan at the University of Tennessee at Knoxville, Patrick Tranel at the University of Illinois at Urbana-Champaign, Tim Willson at UNC and Robigo founder and CEO Andee Wallace. The first greenhouse tests of initial candidates based on RNA interference (RNAi) are expected to be complete by the end of the program’s first year.
Discovery Paired With Chemical Degradation
The project also addresses chemicals already in use. PLOW’s researchers plan living microbial systems in which engineered bacteria and fungi convert agrochemicals and formulation ingredients into biomass before those substances reach food, soil or waterways. The approach is intended to provide a scalable way to reduce environmental and human exposure.
Taken together, PLOW’s discovery and degradation work fits two of the four technical areas in STREAM’s program plan: AI-assisted discovery of next-generation herbicides and the removal or degradation of agricultural chemicals. The other areas cover precision and nonchemical weed control, including laser ablation and miniature autonomous weeders, and improved monitoring of herbicides and associated chemicals. The U.S. Department of Health and Human Services launched STREAM on August 17, 2026, framing it as an effort to protect farm workers, consumers and the environment while maintaining crop yields.
The award ties generative AI’s value to a demanding experimental test: a large candidate library is useful only if screening and greenhouse studies identify molecules that work under agricultural conditions while meeting cost, resistance and safety constraints. PLOW’s degradation work addresses the other side of the problem, aiming to reduce exposure to existing agrochemicals as new alternatives are developed.
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