Data-Driven Approach for Designing Eco-Friendly Heterocyclic Compounds for the Soil Microbiome

Soil microbiota plays crucial roles in maintaining the health, productivity, and nutrient cycling of terrestrial ecosystems. The persistence and prevalence of heterocyclic compounds in soil pose significant risks to soil health. However, understanding the links between heterocyclic compounds and microbial responses remains challenging due to the complexity of microbial communities and their various chemical structures. This study developed a machine-learning approach that integrates the properties of chemical structures with the diversity of soil bacteria and functions to predict the impact of heterocyclic compounds on the microbial community and improve the design of eco-friendly heterocyclic compounds. We screened the key chemical structures of heterocyclic compoundsparticularly those with topological polar surface areas (2 or 111.3–154.1 Å2), carboxyl groups, and dissociation constant, which maintained high soil bacterial diversity and functions, revealing threshold effects where specific structural parameters dictated microbial responses. These eco-friendly compounds stabilize communities and increase beneficial carbon and nitrogen cycle functions. By applying these design parameters, we quantitatively assessed the eco-friendliness scores of 811 heterocyclic compounds, providing a robust foundation for guiding future applications. Our study disentangles the critical chemical structure-related properties that influence the soil microbial community and establishes a computational framework for designing eco-friendly compounds with ecological benefits from an ecological perspective.土壤微生物群在维持陆地生态系统的健康、生产力与养分循环中发挥着至关重要的作用。土壤中杂环化合物（heterocyclic compounds）的持续存在与广泛分布，对土壤健康构成了显著威胁。然而，由于微生物群落结构复杂且杂环化合物的化学结构多样，阐明杂环化合物与微生物响应之间的关联仍颇具挑战。本研究开发了一种机器学习方法，将化学结构属性与土壤细菌多样性及功能相整合，用以预测杂环化合物对微生物群落的影响，并优化生态友好型杂环化合物的设计。我们筛选出了可维持较高土壤细菌多样性与功能的杂环化合物关键化学结构特征——尤其是拓扑极表面积（topological polar surface areas）为2或111.3~154.1 Ų、含有羧基（carboxyl groups）以及具备解离常数（dissociation constant）的结构，揭示了由特定结构参数决定微生物响应的阈值效应。这类生态友好型化合物可稳定微生物群落，并增强有益的碳、氮循环功能。通过应用上述设计参数，我们对811种杂环化合物的生态友好性评分进行了定量评估，为指导其未来应用提供了坚实的理论基础。本研究厘清了影响土壤微生物群落的关键化学结构相关属性，并从生态学视角构建了可开发具备生态效益的生态友好型化合物的计算框架。