Challenge:
- Global agriculture faces major crop losses due to pests & insects.
- Traditional pesticide discovery is slow, costly, and heavily experimental.
- Growing concerns: resistance, safety, and environmental sustainability.
Our Goal:
Design next-generation pesticide & insecticide inhibitors using AI + physics-based models, reducing time and cost before experimental validation.
AI + Physics-Based Approach:
- AI Models
- Deep learning trained on agrochemical datasets.
- Generative algorithms to design novel scaffolds.
- Physics-Based Models
- Docking & molecular dynamics for binding affinity.
- Quantum chemistry for stability & energy scoring.
Key Outcomes:
- Explored millions of virtual molecules → narrowed to top candidates.
- Designed novel chemotypes beyond existing pesticides.
- Predicted high potency & selectivity vs. pest targets.
- In silico tox: favorable safety profiles.
Status:
- Computational pipeline complete.
- Experimental validation pending (synthesis & biological testing next).
- Potential for safer, sustainable crop protection solutions.
Conclusion:
This project demonstrates how AI + physics-based models can:
- Accelerate agrochemical discovery.
- Deliver innovative, environmentally responsible inhibitors.
- Cut reliance on costly broad-screening campaigns.