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

Area of Science:

• Environmental Chemistry
• Soil Science
• Microbial Ecology

Background:

• Soil microbiota are vital for ecosystem health and nutrient cycling.
• Heterocyclic compounds in soil can negatively impact soil health and microbial communities.
• Predicting microbial responses to diverse chemical structures is complex.

Purpose of the Study:

• To develop a machine-learning model predicting the impact of heterocyclic compounds on soil microbial communities.
• To identify key chemical structure properties that promote soil bacterial diversity and functions.
• To guide the design of eco-friendly heterocyclic compounds with ecological benefits.

Main Methods:

• Integrated machine learning with soil bacterial diversity and function data.
• Screened heterocyclic compounds based on topological polar surface area, carboxyl groups, and dissociation constant.
• Quantitatively assessed eco-friendliness scores for 811 compounds.

Main Results:

• Identified threshold effects of specific chemical structures on microbial responses.
• Compounds with certain topological polar surface areas (<74.2 Ų or 111.3-154.1 Ų) and carboxyl groups maintained high bacterial diversity and functions.
• Eco-friendly compounds were found to stabilize soil communities and enhance carbon and nitrogen cycling.

Conclusions:

• Disentangled critical chemical structure-property relationships influencing soil microbial communities.
• Established a computational framework for designing ecologically beneficial heterocyclic compounds.
• Provided a foundation for future applications of eco-friendly compounds in soil management.