Machine learning-based QSAR modeling assay for the nitrification inhibition of 2,4,5-trichloroaniline-derived eco-friendly Schiff bases

Key Points

• The aim is to develop machine learning models to predict the nitrification inhibition effects of Schiff bases derived from 2,4,5-trichloroaniline.
• Utilized machine learning techniques to analyze chemical structures
• Developed QSAR models for predicting nitrification inhibition
• Evaluated the efficacy of various Schiff bases
• Identified specific Schiff bases with high nitrification inhibition potential
• Demonstrated machine learning's effectiveness in predicting toxicity
• Provided insights into eco-friendly alternatives for chemical inhibitors