预测定量阅读跨结构-属性关系建模的保留时间 (日志)
Shilpayan Ghosh1, Mainak Chatterjee1, Kunal Roy1
1Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, India.
这项研究开发了一种定量跨读结构-属性关系 (q-RASPR) 模型,用于预测HPLC分析中的农药保留时间. 该模型准确地预测了保留时间,并有助于识别生态毒性潜力.
科学领域:
- 环境化学环境化学
- 计算化学的计算化学
- 毒理学 毒理学 毒理学
背景情况:
- 在HPLC中的保留时间与脂性相关,这是生态毒性的关键因素.
- 量化结构与属性关系 (QSPR) 模型在外部预测性方面存在局限性.
- 需要新的建模方法来准确预测环境的化学特性.
研究的目的:
- 开发和验证使用HPLC数据预测农药保留时间 (log tR) 的定量读横结构-属性关系 (q-RASPR) 模型.
- 评估模型的外部可预测性和可解释性,用于环境风险评估.
- 探索q-RASPR的实用性,作为预测生态毒性潜力的实验方法的经济有效替代方案.
主要方法:
- 使用了823种环境重要农药残留物的数据集.
- 采用0D-2D描述符和阅读交叉衍生的相似性描述符用于模型生成.
- 开发了一种部分最小方程 (PLS) 模型,并使用经合组织推的内部和外部指标来验证它.
主要成果:
- 开发的q-RASPR模型表现出极佳的性能:R2 = 0.82,Q2LOO = 0.81,Q2F1 = 0.84.
- 该模型在外部预测性方面显著优于之前报告的QSPR模型.
- 脂性被确定为影响保留时间的主要描述因素,其他因素如多重键和图密度也起着重要作用.
结论:
- q-RASPR模型是一个强大的,外部可预测的,可解释的工具,用于保留时间的预测.
- 这种方法为预测生态毒性潜力的实验方法提供了具有成本效益和效率的替代方案.
- q-RASPR在环境风险评估和化学安全评估中具有很强的可转移性和应用潜力.
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