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Predicting the toxicity of multicomponent complex mixtures using a multi-feature fusion-based machine learning model
Yuanfan Zhao1, Renyong Jia2, Jing Zhang1
1Key Laboratory of Water Pollution Control and Wastewater Resource of Anhui Province, College of Environment and Energy Engineering, Anhui Jianzhu University, Hefei, China; Anhui Gaodi Technology Co., LTD, Luan, China.
Aquatic Toxicology (Amsterdam, Netherlands)
|August 6, 2026
Summary
A new interpretable machine learning model (CIMM) accurately predicts complex chemical mixture toxicity in aquatic environments. This framework integrates concentration addition (CA) and independent action (IA) with molecular descriptors, outperforming traditional models.
Area of Science:
- Environmental Chemistry
- Ecotoxicology
- Machine Learning
Background:
- Complex chemical mixtures in aquatic environments pose significant risks to ecosystems and human health.
- Predicting the combined toxicity of mixtures is crucial for risk assessment but challenging for standard models like concentration addition (CA) and independent action (IA) due to complex interactions.
Purpose of the Study:
- To develop a novel, interpretable machine learning model (CIMM) for predicting the combined toxicity of complex chemical mixtures.
- To integrate traditional toxicological models (CA and IA) with molecular descriptors for enhanced prediction accuracy.
Main Methods:
- A combined interpretable machine learning model (CIMM) was developed, integrating CA and IA predictions with molecular descriptors.
- Model stability was assessed using ten random seeds, and interpretability was analyzed via mutual information, partial dependence plots, and SHAP values.
- A dual-metric applicability domain was established, and generalization performance was evaluated on an independent external validation set.
Main Results:
- The CIMM framework achieved a mean test-set R² of 0.9080 ± 0.0127, significantly outperforming CA alone and models using only molecular descriptors.
- CIMM improved R² by 32.2% and decreased RMSE by 46.4% compared to CA alone.
- In-domain samples showed a higher external validation R² (0.768) than out-of-domain samples (0.352), indicating reliable predictions within the applicability domain.
Conclusions:
- The CIMM framework offers a high-accuracy, interpretable, and applicability-domain-constrained approach for predicting the combined toxicity of aquatic chemical mixtures.
- This model effectively addresses the limitations of traditional methods in handling complex mixture toxicity, regardless of interaction types (synergism or antagonism).