标题： 佩斯蒂-基因：释放生成分子方法用于毒性感知的农药设计 显示英文标题

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

摘要： 全球气候变化已降低了作物的抗性和农药的有效性，使得依赖合成农药成为不可避免的选择，尽管其广泛使用对健康和环境构成重大风险。 虽然这些农药仍然是害虫管理中的关键工具，但以前在农药和农业领域的机器学习应用主要集中在分类或回归上，未能解决生成新分子结构或设计新型候选物的基本挑战。 在本文中，我们提出了 Pesti-Gen，一种基于变分自编码器的新生成模型，首次旨在创建具有优化特性的农药候选物。 具体而言，Pesti-Gen利用两阶段学习过程：一个初始的预训练阶段，用于捕捉通用的化学结构表示，随后是一个微调阶段，用于整合毒性特定信息。 该模型同时优化多个毒性指标，例如（1）牲畜毒性以及（2）水生毒性，以生成环境友好的农药候选物。 值得注意的是，Pesti-Gen在生成新分子结构方面达到了约68%的结构有效性，证明了该模型在生成优化且可行的农药候选物方面的有效性，从而为更安全和可持续的害虫管理解决方案提供了新途径。 显示英文摘要

摘要： 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.