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基于毒素鉴定器的机器学习集成Tox21测试读取器官系统特异性致癌性预测

Chi-Yun Chen¹, Wei-Chun Chou², Venkata Nithin Kamineni¹

¹Department of Environmental and Global Health, College of Public Health and Health Professions, University of Florida, Gainesville, FL, 32611, United States; Center for Environmental and Human Toxicology, University of Florida, FL, 32611, United States.

Environmental pollution (Barking, Essex : 1987)

|December 13, 2025

概括

使用机器学习开发器官特异性致癌性模型可以改善化学品安全评估. 这些模型可以预测器官层面的癌症风险,有助于药物开发和公共卫生保护.

科学领域:

毒理学毒理学毒理学
计算化学的计算化学
机器学习机器学习

背景情况:

传统的致癌性测试是缓慢的,昂贵的,并引发了伦理方面的担忧.
现有的定量结构-活性关系 (QSAR) 模型缺乏对药物开发至关重要的器官特定预测.
需要准确的器官水平致癌性预测工具.

研究的目的:

开发和验证基于机器学习的QSAR模型,用于预测器官特异性致癌性.
整合多样化的数据来源,包括Tox21生物活性,化学描述符和指纹.
提供一个快速选工具,以优先考虑化学安全评估.

主要方法:

策划了945种化合物的数据集,其中包括五个器官系统的哺乳动物致癌率数据.
使用RDKit描述符和分子指纹 (ECFP6,FCFP6,MACCS) 的机器学习算法 (CatBoost,神经网络).
整合Tox21生物活性终点,特别是CYP450抑制,进入模型开发.

主要成果:

对于器官特异性致癌性,已达到可接受到良好的预测性能 (F1 = 0.68-0.88,AUC = 0.64-0.83).
确定了关键的预测特征:子结构指纹用于内分泌/呼吸系统和肝胆/泌尿系统的物理化学描述.
Tox21生物活性终点,特别是CYP450抑制,对于表皮致癌性预测至关重要.

结论:

机器学习模型有效地预测器官特定的致癌潜力,增强化学品风险评估.
开发的模型为快速选和优先考虑化学品提供了有价值的工具.
这项工作在预测器官水平致癌性方面取得了重大进展.

关键词:

致癌性致癌性致癌性功能重要性功能重要性机器学习是机器学习.器官系统器官系统定量结构 - 活动关系 (QSAR)托克斯2121是什么意思

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