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基于扩大化学空间的机器学习模型用于选致癌化学物质

Chao Wu¹, Jingwen Chen¹, Yuxuan Zhang¹

¹Key Laboratory of Industrial Ecology and Environmental Engineering (Ministry of Education), Dalian Key Laboratory on Chemicals Risk Control and Pollution Prevention Technology, School of Environmental Science and Technology, Dalian University of Technology, Dalian 116024, China.

Chemical research in toxicology

|June 27, 2025

概括

新的机器学习模型使用扩大数据集和强大的适用性领域特征精确选致癌化学物质. 该工具有助于为更安全的管理和监管应用优先考虑化学品.

科学领域:

毒理学毒理学毒理学
计算化学计算化学
数据科学数据科学数据科学

背景情况:

机器学习 (ML) 模型对于化学品安全和管理至关重要.
之前的模型由于数据集小以及缺乏适用性域 (AD) 描述而存在局限性.
监管应用需要可靠的模型和定义的AD.

研究的目的:

开发和验证改进的ML模型,用于选致癌化学物质.
使用结构-活动景观 (SAL) 方法 (AD_SAL) 定义适用性域 (AD).
将验证的模型应用于大型化学数据集进行风险评估.

主要方法:

策划了1697种化合物的扩大数据集 (940种致癌物,757种非致癌物).
使用12个分子指纹,4个ML算法和2个图形神经网络构建了查模型.
使用AD_SAL方法定义了最佳模型的AD.

主要成果:

使用PubChem指纹的最佳随机森林模型在接收器操作特征曲线下的面积达到86.2%.
与之前的努力相比,该模型表现出了卓越的性能.
AD_SAL方法提供了强大的规范性特征.

结论:

开发的ML模型与AD_SAL相结合,是识别致癌化学物质的有希望的工具.
成功选了1282种来自IECSC的化学品和841种塑料添加剂.
促进化学品优先排序,以确保合理的化学品管理和监管监督.

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