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Updated: Jan 10, 2026

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预测化学暴露体的毒性和生物活性:血液暴露体数据库的案例研究

Ankita Dutta¹, Dinesh Barupal¹

¹Integrated Data Science Laboratory for Metabolomics and Exposomics, Department of Environmental Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.

bioRxiv : the preprint server for biology

|November 26, 2025

概括

计算毒理学模型预测血液暴露组中的化学危险. 机器学习准确地优先考虑化学物质进行进一步的毒理学研究,解决传统实验室测试的局限性.

科学领域:

环境健康环境健康
计算毒理学计算毒理学
化学信息学化学信息学

背景情况:

人类接触到许多化学物质,其中许多是在血液中发现的.
传统上,评估化学危害需要进行昂贵且耗时的实验室实验.
对于有效评估化学毒性的计算方法有极大需求.

研究的目的:

开发和应用计算模型来预测化学生物活性和危害分类.
在血液暴露基因数据库中对化学物质进行优先排序,用于毒理学研究.

主要方法:

训练有素的直接传递信息的神经网络 (D-MPNN) 使用来自Tox21计划的化学结构和生物活性数据 (9,458种化合物,148个测试).
开发了一种使用化学结构 (264,601) 的二次模型,用于急性口服毒性的联合国GHS分类.
将48个受过训练的模型应用于来自血液表皮细胞数据库的58,673种化学物质.

主要成果:

模型实现了强大的预测性能,47个Tox21试验的平均AUC超过0.80.
成功预测了血液暴露细胞数据库中的化学品的生物活性和GHS危险分类.
启用可扩展的化学品在体中优先进行毒理学评估.

结论:

计算模型,特别是D-MPNNs,提供了一个可扩展的解决方案,以优先考虑血液暴露组中的化学物质.
这些预测模型通过识别未经研究的暴露来显著提高毒理风险评估.
该研究为推进计算毒理学研究提供了有价值的数据和代码.

关键词:

剧烈毒性有毒性生物测试生物测试血液暴露细胞体化工制品是一种深度学习 (Deep Learning) 是一种深度学习.暴露组体是指暴露组体.预测性毒理学预测性毒理学这是一个PubChem的产品.托克斯2121是什么意思联合国-GHS分类的分类.

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