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

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 toxicity properties. 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. Pesti-Gen achieves approximately 68% structural validity, demonstrating its potential to generate viable pesticide candidates while balancing toxicity reduction and synthetic feasibility. By integrating machine learning with ecological safety constraints, this work contributes toward safer and more sustainable pest management solutions.Clinical relevance-This work focuses on environmental and public health by leveraging machine learning to design pesticides with reduced toxicity risks, mitigating exposure-related health hazards.