MIT researchers will lead an up to $53 million effort, awarded by the U.S. Advanced Research Projects Agency for Health (ARPA-H) as part of its STREAM (Systems for Tracking and Resilience in Efficient Agricultural-input Management) program, to develop next-generation weed killers that help farmers and protect human health.
More than 1 billion pounds of herbicide are used in the United States every year. Herbicides help farmers by preventing significant crop loss due to weeds, and they enable regenerative farming practices that protect the soil, but some weeds are becoming resistant to the most common herbicides.
Exposure to herbicides may contribute to increased risk of cancer, metabolic and endocrine disorders, and other adverse health outcomes. Farm workers can be exposed from frequent herbicide use, but many U.S. residents are also exposed to these chemicals in small amounts through food and drinking water.
The MIT-led research team will develop new generative artificial intelligence tools to help them discover herbicide alternatives that are more effective and developed with human health in mind. Such alternatives could also help to reduce U.S. dependency on imported phosphorus, which is a critical ingredient in a major herbicide, glyphosate.
“With herbicides like glyphosate and atrazine, there’s a real crisis because of resistance mutations that are coming,” says Christopher Voigt, head of MIT’s Department of Biological Engineering. “And if you combine the resistance mutations with issues around national security of the supply chain of the precursors to herbicides, we could find ourselves without these core agrochemicals that are absolutely essential for getting the high crop yields and economical prices that the United States now sees.”
Voigt is the principal investigator for the project, known as Precision Learning for On-target Weed control (PLOW). The team is also expected to include researchers from the Broad Institute of MIT and Harvard, the University of North Carolina at Chapel Hill, the University of Tennessee at Knoxville, the University of Illinois at Urbana-Champaign, and Robigo.
The researchers are grouped into three teams that will tackle different aspects of the project, aligned with the STREAM Technical Areas. The generative AI group will develop models that can predict molecules — either chemical compounds or small interfering RNA (siRNA) — that disrupt the same plant enzymes targeted by glyphosate and other herbicides.
Glyphosate kills weeds by interfering with an enzyme called EPSP synthase, which plants need to produce several critical amino acids. After many years of herbicide use, some weeds have begun to acquire genetic mutations that allow them to become resistant to those herbicides, which makes them less effective and poses a significant threat to crop yields.
“There’s a real need for new herbicides because of resistance mutations that have been arising,” Voigt says. “There are also concerns about herbicides’ possible impacts on the environment and on humans, so it’s important to identify alternative ways of making herbicides, which as a general class of molecule are really critical to agriculture.”
The MIT-led team plans to design generative AI models that will be able to predict molecules that can help prevent resistance from arising. The models will also optimize for other features such as low cost of synthesis, low risk of inducing resistance, and low risk of harming humans or causing ecological damage.
The group developing these AI models is led by Connor Coley, the Warren K. Lewis Associate Professor of Chemical Engineering at MIT; Regina Barzilay, the School of Engineering Distinguished Professor for AI and Health at MIT; and Alexander Tropsha, the K.H. Lee Distinguished Professor in the UNC Eshelman School of Pharmacy.
The models are expected to generate billions of possible candidates, which will then be tested by a second research team. This high-throughput screening group will be led by Angela Koehler, the Charles W. and Jennifer C. Johnson Professor of Biological Engineering at MIT, and Jaime Cheah, the associate director of scientific outreach and collaborative screening in the Center for the Development of Therapeutics at the Broad Institute.
“Those libraries will be screened both to identify those molecules that hit their target, but also to then provide all of that screening data back to the computational approach in order to improve it,” Voigt says.
Once the candidate pools are narrowed down, researchers on a third team will begin greenhouse studies to evaluate how each molecule performs under different agricultural conditions. That team will be led by Neal Stewart and Scott Lenaghan, co-directors of the Center for Agricultural Synthetic Biology at UTK; Patrick Tranel, the Tom and Peggy Ainsworth Professor of Crop Sciences at the University of Illinois; Tim Willson, the Harold Kohn Distinguished Professor at the UNC Eshelman School of Pharmacy; and Andee Wallace PhD ’20, CEO and founder of Robigo.
The first greenhouse tests of initial RNAi candidates are expected to be complete by the end of the first year of the program.
As part of this project, the research team will also develop living microbial systems that degrade agrochemicals before they reach food, soil, or waterways. Engineered bacteria, fungi, and srRNA will convert chemicals and formulation ingredients into biomass, providing a scalable strategy to reduce environmental and human exposure.
This research is funded by the Advanced Research Projects Agency for Health (ARPA-H) under agreement number 140D042690030. The views and conclusions contained in this article are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the United States government.
