基于机器学习的q-RASAR对有机分子生物度因子的预测,根据经济合作与发展组织指南305进行估计
Souvik Pore1, Alexia Pelloux2, Mainak Chatterjee1
1Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, 188 Raja S C Mullick Road, 700032 Kolkata, India.
Journal of hazardous materials
|September 7, 2024
概括
本研究引入了一种定量阅读跨结构-活性关系 (q-RASAR) 方法来预测化学生物度因子 (BCF). q-RASAR方法有效地预测新化学品的BCF值,增强环境风险评估.
科学领域:
- 环境化学环境化学
- 毒理学 毒理学 毒理学
- 计算化学的计算化学
背景情况:
- 准确预测化学生物度因子 (BCF) 对环境风险评估至关重要.
- 现有的方法可能缺乏对新化学物质的预测能力.
研究的目的:
- 开发和验证一个定量阅读跨结构-活动关系 (q-RASAR) 模型,用于预测BCF值.
- 用各种机器学习算法评估q-RASAR的性能.
主要方法:
- 使用了575个化合物的数据集,通过经合组织测试指南305.5进行测试.
- 采用了部分最小平方回归和其他监督机器学习算法.
- 在RASAR描述符中包含相似性函数.
主要成果:
- 最初的q-RASAR模型在训练 (R2=0.71,Q2LOO=0.68) 和测试集 (Q2F1=0.77) 上表现出强的表现.
- 替代算法也表现出了有利的结果,R2和Q2F1值始终高于0.7.
- 模型表现出对新的未识别化学物质具有很高的预测能力.
结论:
- q-RASAR方法显著提高了对新化学品的预测能力.
- 该方法的有效性独立于所使用的特定回归算法.
- 这种方法为环境安全评估提供了有价值的工具.
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