机器学习用于预测基对水性有机化学品的反应性
Youheng Liang1, Xiaoliu Huangfu1, Ruixing Huang1
1Key Laboratory of Eco-Environments in Three Gorges Reservoir Region, Ministry of Education, College of Environment, and Ecology, Chongqing University, Chongqing 400044, China.
Journal of hazardous materials
|May 12, 2024
概括
机器学习模型使用摩根指纹和Mordred描述符准确地预测有机污染物的反应性. 数据组合策略提高了准确性,导致了最佳的LightGBM和随机森林模型,用于更广泛的预测.
科学领域:
- 环境化学环境化学
- 计算化学计算化学
- 机器学习应用 机器学习应用
背景情况:
- 有机污染物带来环境风险,了解它们的反应性对于风险评估至关重要.
- 预测有机污染物的反应性,特别是它们与自由基的反应速率常数 (logk),对于环境修复和管理至关重要.
- 传统的方法来确定污染物反应性往往是耗时和资源密集的.
研究的目的:
- 开发准确和高效的机器学习 (ML) 模型,用于预测自由基介导的有机污染物反应率常数 (logk).
- 调查不同分子描述器的有效性,特别是摩根指纹 (MF) 和莫德雷德描述器 (MD),结合各种ML算法.
- 通过实施数据组合策略来应对有限的样本规模的挑战,以提高预测准确性和减轻过度拟合.
主要方法:
- 使用摩根指纹 (MF) 和莫德雷德描述符 (MD) 来表示分子结构.
- 利用了一系列机器学习模型,包括光梯度增强机 (LightGBM) 和随机森林 (RF).
- 实施数据组合策略,以创建统一的数据集,提高模型性能和通用性.
- 应用了SHapley添加式扩展 (SHAP) 来实现模型的解释性,并确定了影响反应性的关键分子特征.
- 进行适用性领域分析,以确保新化合物预测的可靠性.
主要成果:
- 在统一数据集上训练的MF的LightGBM模型和MD的RF模型被确定为最佳预测模型.
- SHAP分析显示,MF-LightGBM模型有效地捕捉了电子提取/捐赠群体的影响,而MD-RF模型强调了自相关性,步数和信息内容描述者的重要性.
- 该研究强调了pH对污染物反应性预测的显著贡献.
- 适用性领域分析证实了开发的模型对各种化学结构的广泛和可靠的预测能力.
结论:
- 机器学习,特别是当集成有效的描述符和数据策略时,提供了一种强大的方法来准确预测有机污染物的反应性.
- 开发的模型为环境风险评估和修复策略的设计提供了强大而有效的工具.
- 成功开发了一款用于计算logk的实用web应用程序,促进了研究结果的更广泛的可访问性和应用.
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