We've published work in Bioorganic Chemistry applying our AI drug-design pipeline to a very different target, herbicide discovery. The molecule here is 4-hydroxyphenylpyruvate dioxygenase (HPPD), one of the few commercially successful modern herbicide targets, and the goal was to generate genuinely new inhibitor scaffolds rather than variations on the existing triketone chemistry. Data is what makes this hard. Generative AI models are usually trained on large pharmaceutical datasets, but specialized enzyme targets like HPPD have far fewer known active compounds, and agricultural molecules have to satisfy physicochemical requirements like solubility, foliar uptake, and systemic movement in plants that pull the useful chemical space away from where medicinal-chemistry-trained models are comfortable.