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Discovery of a New Class of Lipophilic Pyrimidine-Biphenyl Herbicides Using an Integrated Experimental-Computational Approach.

Yi-Tao YanYinglu ChenHanxian HuYouwei JiangZhengzhong KangJun Wu
Published in: Molecules (Basel, Switzerland) (2024)
Herbicides are useful tools for managing weeds and promoting food production and sustainable agriculture. In this study, we report on the development of a novel class of lipophilic pyrimidine-biphenyl (PMB) herbicides. Firstly, three PMBs, Ia , IIa , and IIIa , were rationally designed via a scaffold hopping strategy and were determined to inhibit acetohydroxyacid synthase (AHAS). Computational simulation was carried out to investigate the molecular basis for the efficiency of PMBs against AHAS. With a rational binding mode, and the highest in vitro as well as in vivo potency, Ia was identified as a preferable hit. Furthermore, these integrated analyses guided the design of eighteen new PMBs, which were synthesized via a one-step Suzuki-Miyaura cross-coupling reaction. These new PMBs, Iba-ic , were more effective in post-emergence control of grass weeds compared with Ia . Interestingly, six of the PMBs displayed 98-100% inhibition in the control of grass weeds at 750 g ai/ha. Remarkably, Ica exhibited ≥ 80% control against grass weeds at 187.5 g ai/ha. Overall, our comprehensive and systematic investigation revealed that a structurally distinct class of lipophilic PMB herbicides, which pair excellent herbicidal activities with new interactions with AHAS, represent a noteworthy development in the pursuit of sustainable weed control solutions.
Keyphrases
  • artificial intelligence
  • small molecule
  • machine learning
  • high throughput