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Cheminformatics and artificial intelligence for accelerating agrochemical discovery.

Yannick Djoumbou-Feunang1, Jeremy Wilmot2, John Kinney1

  • 1Corteva Agriscience, Farming Solutions and Digital, Indianapolis, IN, United States.

Frontiers in Chemistry
|December 14, 2023
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Summary
This summary is machine-generated.

The cost-benefit of pesticides has declined due to resistance and regulations. Artificial intelligence and cheminformatics offer innovative solutions for discovering sustainable crop protection agents to meet rising global food demand.

Keywords:
DMTA cycleagrochemicalsartificial intelligencecheminformaticslead generationlead optimizationmachine learningsustainability

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Area of Science:

  • Agricultural Science
  • Computational Chemistry
  • Toxicology

Background:

  • Global pesticide use cost-benefit analysis shows a decline post-2007, influenced by pest resistance, lack of novel chemistries, and regulatory pressures.
  • Increasing global population necessitates higher food production, driving demand for effective pesticides to enhance crop yields.
  • Stringent regulatory environments require innovative, cost-effective, and time-efficient methods for developing new crop protection agents.

Purpose of the Study:

  • To review the crop protection discovery pipeline.
  • To explore how cheminformatics and artificial intelligence (AI) can address challenges in agrochemical discovery.
  • To highlight the potential of AI and cheminformatics in developing novel and sustainable agrochemical products.

Main Methods:

  • Review of traditional agrochemical discovery processes.
  • Analysis of cheminformatics applications in molecule discovery.
  • Exploration of artificial intelligence (AI) tools and techniques for virtual screening and lead optimization.

Main Results:

  • Advances in AI and cheminformatics significantly enhance decision-making in discovering bioactive molecules.
  • AI and cheminformatics enable rapid virtual investigation of vast chemical spaces, accelerating lead generation and optimization.
  • The application of AI and cheminformatics in agrochemical research is less prevalent than in drug discovery.

Conclusions:

  • AI and cheminformatics offer powerful tools to accelerate the discovery of novel, sustainable agrochemical solutions.
  • Integrating these technologies can overcome challenges posed by pest resistance and regulatory hurdles.
  • Future agrochemical development can benefit from virtual environments for rapid, cost-effective screening and synthesis suggestions.