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蜜蜂毒素:用于小分子对蜜蜂毒性分类的新基准数据集。

ApisTox: a new benchmark dataset for the classification of small molecules toxicity on honey bees.

作者信息

Adamczyk Jakub, Poziemski Jakub, Siedlecki Pawel

机构信息

AGH University of Krakow, Department of Computer Science, Cracow, Poland.

Institute of Biochemistry and Biophysics, Polish Academy of Sciences, Warsaw, Poland.

出版信息

Sci Data. 2025 Jan 2;12(1):5. doi: 10.1038/s41597-024-04232-w.

DOI:10.1038/s41597-024-04232-w
PMID:39747220
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11696378/
Abstract

The global decline in bee populations poses significant risks to agriculture, biodiversity, and environmental stability. To bridge the gap in existing data, we introduce ApisTox, a comprehensive dataset focusing on the toxicity of pesticides to honey bees (Apis mellifera). This dataset combines and leverages data from existing sources such as ECOTOX and PPDB, providing an extensive, consistent, and curated collection that surpasses the previous datasets. ApisTox incorporates a wide array of data, including toxicity levels for chemicals, details such as time of their publication in literature, and identifiers linking them to external chemical databases. This dataset may serve as an important tool for environmental and agricultural research, but also can support the development of policies and practices aimed at minimizing harm to bee populations. Finally, ApisTox offers a unique resource for benchmarking molecular property prediction methods on agrochemical compounds, facilitating advancements in both environmental science and chemoinformatics. This makes it a valuable tool for both academic research and practical applications in bee conservation.

摘要

全球蜜蜂数量的减少给农业、生物多样性和环境稳定性带来了重大风险。为了填补现有数据的空白,我们引入了ApisTox,这是一个专注于农药对蜜蜂(西方蜜蜂)毒性的综合数据集。该数据集整合并利用了来自ECOTOX和PPDB等现有来源的数据,提供了一个广泛、一致且经过整理的集合,超越了以往的数据集。ApisTox包含了大量数据,包括化学物质的毒性水平、它们在文献中发表的时间等细节,以及将它们与外部化学数据库链接的标识符。这个数据集不仅可以作为环境和农业研究的重要工具,还可以支持旨在尽量减少对蜜蜂种群危害的政策和实践的制定。最后,ApisTox为农用化学品化合物的分子性质预测方法提供了一个独特的基准资源,促进了环境科学和化学信息学的进步。这使其成为蜜蜂保护学术研究和实际应用的宝贵工具。

相似文献

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ApisTox: a new benchmark dataset for the classification of small molecules toxicity on honey bees.蜜蜂毒素:用于小分子对蜜蜂毒性分类的新基准数据集。
Sci Data. 2025 Jan 2;12(1):5. doi: 10.1038/s41597-024-04232-w.
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Predicting acute contact toxicity of pesticides in honeybees (Apis mellifera) through a k-nearest neighbor model.通过k近邻模型预测农药对蜜蜂(西方蜜蜂)的急性接触毒性。
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Evaluation of the distribution and impacts of parasites, pathogens, and pesticides on honey bee (Apis mellifera) populations in East Africa.评估寄生虫、病原体和农药在东非蜜蜂(Apis mellifera)种群中的分布和影响。
PLoS One. 2014 Apr 16;9(4):e94459. doi: 10.1371/journal.pone.0094459. eCollection 2014.
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QSTR modeling for qualitative and quantitative toxicity predictions of diverse chemical pesticides in honey bee for regulatory purposes.用于监管目的的蜜蜂中多种化学农药定性和定量毒性预测的定量构效关系建模
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Chemical Stimulants and Stressors Impact the Outcome of Virus Infection and Immune Gene Expression in Honey Bees ().化学刺激物和应激源会影响蜜蜂()中病毒感染和免疫基因表达的结果。
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Does the Honey Bee "Risk Cup" Runneth Over? Estimating Aggregate Exposures for Assessing Pesticide Risks to Honey Bees in Agroecosystems.蜜蜂的“风险杯”是否已满?估算农业生态系统中评估农药对蜜蜂风险的综合暴露量。
J Agric Food Chem. 2016 Jan 13;64(1):13-20. doi: 10.1021/acs.jafc.5b01067. Epub 2015 May 11.

本文引用的文献

1
Free and open-source QSAR-ready workflow for automated standardization of chemical structures in support of QSAR modeling.用于化学结构自动标准化以支持定量构效关系建模的免费开源且适用于定量构效关系的工作流程。
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Learning self-supervised molecular representations for drug-drug interaction prediction.学习用于药物-药物相互作用预测的自监督分子表示。
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Curated mode-of-action data and effect concentrations for chemicals relevant for the aquatic environment.
针对水生环境相关的化学物质,进行作用模式数据和效应浓度的精选。
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SIMPD: an algorithm for generating simulated time splits for validating machine learning approaches.SIMPD:一种用于生成模拟时间分割以验证机器学习方法的算法。
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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.
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A systematic study of key elements underlying molecular property prediction.对分子性质预测背后关键要素的系统研究。
Nat Commun. 2023 Oct 13;14(1):6395. doi: 10.1038/s41467-023-41948-6.
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Pesticide effect on earthworm lethality via interpretable machine learning.通过可解释机器学习研究农药对蚯蚓致死率的影响。
J Hazard Mater. 2024 Jan 5;461:132577. doi: 10.1016/j.jhazmat.2023.132577. Epub 2023 Sep 18.
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Comparing LD/LC Machine Learning Models for Multiple Species.比较多种物种的LD/LC机器学习模型
J Chem Health Saf. 2023 Mar 27;30(2):83-97. doi: 10.1021/acs.chas.2c00088. Epub 2023 Feb 23.
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Revised guidance on the risk assessment of plant protection products on bees (, spp. and solitary bees).关于植物保护产品对蜜蜂( 、 属及独居蜂)风险评估的修订指南
EFSA J. 2023 May 11;21(5):e07989. doi: 10.2903/j.efsa.2023.7989. eCollection 2023 May.
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The Buzz on Insecticides: A Review of Uses, Molecular Structures, Targets, Adverse Effects, and Alternatives.杀虫剂的热议:用途、分子结构、靶标、不良影响及替代品的综述。
Molecules. 2023 Apr 21;28(8):3641. doi: 10.3390/molecules28083641.