Pesti-Gen: Unleashing a Generative Molecule Approach for Toxicity Aware Pesticide Design

2025-01-24

AI Summary

Climate change is enhancing crop resistance and reducing pesticide efficacy while increasing ecological toxicity risks. Method: We propose the first toxicity-aware two-stage variational autoencoder (VAE) framework for de novo pesticide molecular design, integrating molecular graph representations, a pretraining–toxicity fine-tuning paradigm, and a multi-target environmental toxicity prediction module (for livestock and aquatic organisms) to jointly optimize insecticidal activity and low ecological toxicity. Contribution/Results: The generated molecules achieve a structural validity rate of 68%, significantly improving synthetic feasibility and environmental safety. This work pioneers generative pesticide design under multi-dimensional toxicity constraints, establishing a practical, AI-driven paradigm for green pesticide development.

Technology Category

• Machine Learning: Efficient ML / Green AI
• Search and Optimization: Evolutionary Computation
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Global climate change has reduced crop resilience and pesticide efficacy, making reliance on synthetic pesticides inevitable, even though their widespread use poses significant health and environmental risks. While these pesticides remain a key tool in pest management, previous machine-learning applications in pesticide and agriculture have focused on classification or regression, leaving the fundamental challenge of generating new molecular structures or designing novel candidates unaddressed. In this paper, we propose Pesti-Gen, a novel generative model based on variational auto-encoders, designed to create pesticide candidates with optimized properties for the first time. Specifically, Pesti-Gen leverages a two-stage learning process: an initial pre-training phase that captures a generalized chemical structure representation, followed by a fine-tuning stage that incorporates toxicity-specific information. The model simultaneously optimizes over multiple toxicity metrics, such as (1) livestock toxicity and (2) aqua toxicity to generate environmentally friendly pesticide candidates. Notably, Pesti-Gen achieves approximately 68% structural validity in generating new molecular structures, demonstrating the model's effectiveness in producing optimized and feasible pesticide candidates, thereby providing a new way for safer and more sustainable pest management solutions.

Problem

• Safer Pesticides
• Climate Change
• Environmental Impact

Innovation

• Molecular Design
• Environmental Friendly Pesticides
• Chemical Structure Learning