使用ML-DL-ens模型预测有机化合物的水中毒性:机器学习和深度学习的综合方法
Yan Li1, Guo-Lin Fu2, Lu-Yan Zhang1
1School of Environmental Science and Engineering, Yancheng Institute of Technology, Yancheng 224051, China.
Ecotoxicology and environmental safety
|January 29, 2026
概括
一个新的ML-DL-ens模型准确地预测有机污染物的水生毒性. 这种先进的框架可以提高预测的准确性,并识别关键的分子结构,帮助环境保护工作.
科学领域:
- 环境科学 环境科学
- 计算化学计算化学
- 毒理学 毒理学 毒理学
背景情况:
- 水生生态系统面临有机污染物的威胁,需要有效的毒性评估.
- 对于毒性预测的传统机器学习方法在特征工程和概括方面存在局限性.
- 需要先进的模型来提高水生物毒性识别的准确性和可解释性.
研究的目的:
- 开发一个集成的机器学习-深度学习框架 (ML-DL-ens) 以提高水生毒性预测.
- 解决经典机器学习模型在预测有机化合物的毒性方面的局限性.
- 提供对影响毒性的分子亚结构的机械洞察力.
主要方法:
- 开发了一个组合模型,结合K-最近邻居,支持向量机器,极端梯度增强,随机森林和AttentiveFP.
- 粒子集群优化 (PSO) 用于优化ML-DL-ens模型的重量.
- 用SHAP价值分析来解释和识别关键的分子亚结构.
主要成果:
- ML-DL-ens模型在多个数据集中实现了卓越的性能,具有高的AUC-ROC值 (例如,40小时IGC50集的0.9404).
- 该模型展示了最先进的性能,优于单个模型和其他综合方法.
- SHAP分析提供了对推动毒性预测的分子特征的见解.
结论:
- ML-DL-ens框架为准确和可解释的水生毒性预测提供了一个有希望的方法.
- 这种方法支持对危险化合物的监管优先级,并促进进一步的实验研究.
- 综合模型提高了计算方法在环境风险评估中的可靠性.
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