Atom-Driven and Knowledge-Based Hydrolysis Metabolite Assessment for Environmental Organic Chemicals.

ABSTRACT: The metabolism of environmental organic chemicals often relies on the catalytic action of specific enzymes at the nanoscale, which is critical for assessing their environmental impact, safety, and efficacy. Hydrolysis is one of the primary metabolic and degradation reaction pathways. Traditionally, hydrolysis product identification has relied on experimental methods that are both time-consuming and costly. In this study, machine-learning-based atomic-driven models were constructed to predict the hydrolysis reactions for environmental organic chemicals, including four main hydrolysis sites: N-Hydrolysis, O-Hydrolysis, C-Hydrolysis, and Global-Hydrolysis. These machine learning models were further integrated with a knowledge-based expert system to create a global hydrolysis model, which util

SUBMITTER: Liu Z 

PROVIDER: S-EPMC11767695 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature