用生成模型设计更安全的农药分子，兼顾毒性与有效性。
Pesti-Gen: Unleashing a Generative Molecule Approach for Toxicity Aware Pesticide Design
- 基于变分自编码器构建两阶段生成模型，先学通用结构再优化毒性。
- 生成分子结构有效率达68%，同时降低对牲畜和水生生物的毒性。
- 适合农药研发、绿色化学与可持续农业领域的研究者参考。
全球气候变化降低了作物抗逆性和农药效力，导致合成农药仍不可避免，但其广泛使用带来重大健康与环境风险。尽管农药在虫害管理中仍是关键工具，以往机器学习应用多集中于分类或回归，未解决生成新分子结构的根本挑战。本文提出Pesti-Gen，一种基于变分自编码器的新型生成模型，首次实现农药候选分子的优化设计。该模型采用两阶段学习：先通过预训练获取通用化学结构表征，再经微调融入毒性信息。模型同步优化多项毒性指标，包括牲畜毒性与水生毒性，生成环境友好型农药候选物。实验表明，Pesti-Gen生成分子结构的有效性达约68%，证明其能产出可行且优化的农药设计，为更安全、可持续的害虫管理提供新路径。
原文摘要 · Abstract (English)
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.
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