• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

定量构效关系(QSAR)用于毒性预测中的不确定性、变异性、偏差和影响的识别和描述。

Identification and description of the uncertainty, variability, bias and influence in quantitative structure-activity relationships (QSARs) for toxicity prediction.

机构信息

Liverpool John Moores University, School of Pharmacy and Biomolecular Sciences, Liverpool, England, United Kingdom.

European Commission, Joint Research Centre (JRC), Ispra, Italy.

出版信息

Regul Toxicol Pharmacol. 2019 Aug;106:90-104. doi: 10.1016/j.yrtph.2019.04.007. Epub 2019 Apr 24.

DOI:10.1016/j.yrtph.2019.04.007
PMID:31026540
Abstract

Improving regulatory confidence in, and acceptance of, a prediction of toxicity from a quantitative structure-activity relationship (QSAR) requires assessment of its uncertainty and determination of whether the uncertainty is acceptable. Thus, it is crucial to identify potential uncertainties fundamental to QSAR predictions. Based on expert review, sources of uncertainties, variabilities and biases, as well as areas of influence in QSARs for toxicity prediction were established. These were grouped into three thematic areas: uncertainties, variabilities, potential biases and influences associated with 1) the creation of the QSAR, 2) the description of the QSAR, and 3) the application of the QSAR, also showing barriers for their use. Each thematic area was divided into a total of 13 main areas of concern with 49 assessment criteria covering all aspects of QSAR development, documentation and use. Two case studies were undertaken on different types of QSARs that demonstrated the applicability of the assessment criteria to identify potential weaknesses in the use of a QSAR for a specific purpose such that they may be addressed and mitigation strategies can be proposed, as well as enabling an informed decision on the adequacy of the model in the considered context.

摘要

为了提高对定量构效关系(QSAR)毒性预测的监管信心和接受度,需要评估其不确定性,并确定不确定性是否可接受。因此,确定 QSAR 预测中潜在的不确定性至关重要。基于专家评审,确定了与毒性预测的 QSAR 相关的不确定性、可变性和偏差的潜在来源,以及影响因素。这些因素被分为三个主题领域:与 1)QSAR 的创建、2)QSAR 的描述和 3)QSAR 的应用相关的不确定性、可变性、潜在偏差和影响,同时也展示了其使用的障碍。每个主题领域总共分为 13 个主要关注点,共有 49 个评估标准,涵盖了 QSAR 开发、文件编制和使用的各个方面。进行了两个不同类型的 QSAR 的案例研究,这些研究表明,评估标准可用于确定在特定目的下使用 QSAR 的潜在弱点,以便解决这些弱点,并提出缓解策略,同时能够在考虑到的背景下对模型的充分性做出明智的决策。

相似文献

1
Identification and description of the uncertainty, variability, bias and influence in quantitative structure-activity relationships (QSARs) for toxicity prediction.定量构效关系(QSAR)用于毒性预测中的不确定性、变异性、偏差和影响的识别和描述。
Regul Toxicol Pharmacol. 2019 Aug;106:90-104. doi: 10.1016/j.yrtph.2019.04.007. Epub 2019 Apr 24.
2
Determination of "fitness-for-purpose" of quantitative structure-activity relationship (QSAR) models to predict (eco-)toxicological endpoints for regulatory use.定量构效关系(QSAR)模型用于预测(生态)毒理学终点的适用性判断。
Regul Toxicol Pharmacol. 2021 Jul;123:104956. doi: 10.1016/j.yrtph.2021.104956. Epub 2021 May 9.
3
Uncertainty in QSAR predictions.QSAR 预测中的不确定性。
Altern Lab Anim. 2013 Mar;41(1):111-25. doi: 10.1177/026119291304100111.
4
A Risk Assessment Perspective of Current Practice in Characterizing Uncertainties in QSAR Regression Predictions.当前 QSAR 回归预测中不确定性特征化实践的风险评估视角。
Mol Inform. 2011 Jun;30(6-7):551-64. doi: 10.1002/minf.201000177. Epub 2011 May 5.
5
Methods for reliability and uncertainty assessment and for applicability evaluations of classification- and regression-based QSARs.基于分类和回归的定量构效关系(QSAR)的可靠性和不确定性评估方法以及适用性评估方法。
Environ Health Perspect. 2003 Aug;111(10):1361-75. doi: 10.1289/ehp.5758.
6
Arguments for considering uncertainty in QSAR predictions in hazard and risk assessments.考虑在危害和风险评估中对定量构效关系预测的不确定性。
Altern Lab Anim. 2013 Mar;41(1):91-110. doi: 10.1177/026119291304100110.
7
Reliable and representative in silico predictions of freshwater ecotoxicological hazardous concentrations.可靠且有代表性的淡水生态毒理学危险浓度的计算机预测。
Environ Int. 2020 Jan;134:105334. doi: 10.1016/j.envint.2019.105334. Epub 2019 Nov 21.
8
Ecotoxicity prediction using mechanism- and non-mechanism-based QSARs: a preliminary study.基于机制和非机制的定量构效关系进行生态毒性预测:一项初步研究。
Chemosphere. 2003 Dec;53(9):1053-65. doi: 10.1016/S0045-6535(03)00573-3.
9
Essential and desirable characteristics of ecotoxicity quantitative structure-activity relationships.生态毒性定量构效关系的基本特征与理想特征。
Environ Toxicol Chem. 2003 Mar;22(3):599-607.
10
Internal and external validation of the long-term QSARs for neutral organics to fish from ECOSAR™.ECOSAR™ 中中性有机物对鱼类长期 QSAR 的内部和外部验证。
SAR QSAR Environ Res. 2011 Jul-Sep;22(5-6):545-59. doi: 10.1080/1062936X.2011.569949. Epub 2011 Jul 7.

引用本文的文献

1
Therapeutic exploration potential of adenosine receptor antagonists through pharmacophore ligand-based modelling and pharmacokinetics studies against Parkinson disease.通过基于药效团配体的建模和针对帕金森病的药代动力学研究探索腺苷受体拮抗剂的治疗潜力。
In Silico Pharmacol. 2025 Jan 25;13(1):17. doi: 10.1007/s40203-025-00305-9. eCollection 2025.
2
A benchmark dataset for machine learning in ecotoxicology.用于生态毒理学机器学习的基准数据集。
Sci Data. 2023 Oct 18;10(1):718. doi: 10.1038/s41597-023-02612-2.
3
Guidance for good practice in the application of machine learning in development of toxicological quantitative structure-activity relationships (QSARs).
机器学习在毒理学定量构效关系(QSARs)开发中的应用良好实践指南。
PLoS One. 2023 May 10;18(5):e0282924. doi: 10.1371/journal.pone.0282924. eCollection 2023.
4
Construction of an In Silico Structural Profiling Tool Facilitating Mechanistically Grounded Classification of Aquatic Toxicants.构建一个计算结构特征分析工具,促进基于机制的水生毒物分类。
Environ Sci Technol. 2022 Dec 20;56(24):17805-17814. doi: 10.1021/acs.est.2c03736. Epub 2022 Nov 29.
5
A scheme to evaluate structural alerts to predict toxicity - Assessing confidence by characterising uncertainties.一种评估结构警示以预测毒性的方案 - 通过描述不确定性来评估置信度。
Regul Toxicol Pharmacol. 2022 Nov;135:105249. doi: 10.1016/j.yrtph.2022.105249. Epub 2022 Aug 27.
6
Improving QSAR Modeling for Predictive Toxicology using Publicly Aggregated Semantic Graph Data and Graph Neural Networks.利用公共聚合语义图数据和图神经网络提高预测毒理学中的 QSAR 建模。
Pac Symp Biocomput. 2022;27:187-198.
7
Quo vadis blood protein adductomics?血液蛋白加合物组学何去何从?
Arch Toxicol. 2022 Jan;96(1):79-103. doi: 10.1007/s00204-021-03165-2. Epub 2021 Nov 13.
8
GRADE Guidelines 30: the GRADE approach to assessing the certainty of modeled evidence-An overview in the context of health decision-making.GRADE 指南 30:建模证据确定性评估的 GRADE 方法——在卫生决策背景下的概述。
J Clin Epidemiol. 2021 Jan;129:138-150. doi: 10.1016/j.jclinepi.2020.09.018. Epub 2020 Sep 24.
9
Practices and Trends of Machine Learning Application in Nanotoxicology.机器学习在纳米毒理学中的应用实践与趋势
Nanomaterials (Basel). 2020 Jan 8;10(1):116. doi: 10.3390/nano10010116.