An Ensemble Learning Framework Utilizing Fusion Molecular Fingerprints for Pollutant Removal Prediction in Advanced...

Area of Science:

• Environmental Chemistry
• Computational Chemistry
• Water Treatment Technologies

Background:

• Accurate prediction of pollutant degradation kinetics is crucial for effective water treatment process design and optimization.
• Conventional quantitative structure-activity relationship (QSAR) models face limitations due to incomplete chemical representations from single-molecular fingerprints.

Purpose of the Study:

• To develop an advanced fingerprint-fusion ensemble learning framework for predicting pollutant reactivity in water treatment.
• To enhance the accuracy of pollutant degradation kinetics prediction by integrating diverse molecular features and environmental variables.

Main Methods:

• Developed a fingerprint-fusion ensemble learning framework that decouples prediction tasks using specialized base learners.
• Employed complementary molecular fingerprints to capture compositional, topological, and conformational features of pollutants.
• Integrated base learner predictions with environmental variables using a meta-learner.

Main Results:

• Achieved high predictive accuracy with test R² values of 0.96 for ozonation and 0.80 for zero-valent iron (ZVI) reduction.
• Outperformed the best single-fingerprint baseline model by over 5% in predictive accuracy.
• Identified key compositional, topological, and conformational drivers of pollutant reactivity through multidimensional interpretation analysis.

Conclusions:

• Fingerprint-fusion ensemble learning is a highly effective strategy for predicting and interpreting pollutant reactivity in advanced water treatments.
• The developed framework offers superior predictive performance compared to conventional QSAR models.
• An interactive web platform was created to provide accessible tools for reactivity prediction and mechanistic exploration in water treatment.