Pesti-Gen: Unleashing a Generative Molecule Approach for Toxicity Aware Pesticide Design
Taehan Kim, Wonduk Seo

TL;DR
Pesti-Gen is a novel generative model that creates pesticide molecules optimized for reduced toxicity, combining a two-stage learning process to produce environmentally safer pesticide candidates.
Contribution
It introduces a variational auto-encoder based generative approach with toxicity-aware optimization for pesticide design, addressing a gap in molecular generation.
Findings
Achieves approximately 68% structural validity in generated molecules
Effectively optimizes for livestock and aqua toxicity metrics
Provides a new framework for sustainable pesticide development
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…
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Taxonomy
TopicsPesticide and Herbicide Environmental Studies · Viral Infectious Diseases and Gene Expression in Insects
