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FGTN:基于碎片的图形变压器网络用于预测生殖毒性

Jia-Nan Ren¹, Qiang Chen¹, Hong-Yu-Xiang Ye¹

¹College of Pharmaceutical Sciences, Zhejiang University, 866 Yuhangtang Rd., Hangzhou, 310058, Zhejiang, China.

Archives of toxicology

|September 18, 2024

概括

一个新的基于片段的图形转换器网络 (FGTN) 准确地预测了人类的生殖毒性. 这种计算方法为传统测试提供了一个更快,更道德的替代方案,识别特定的有毒基结构.

科学领域:

计算毒理学计算毒理学
化学安全评估化学安全评估
定量结构-活动关系 (QSAR) 建模.

背景情况:

生殖毒性在化学安全方面带来了重大挑战.
传统的毒性测试是昂贵的,耗时的,并且在道德上存在问题.
现有的in silico模型往往无法充分利用复合拓信息.

研究的目的:

开发一种基于碎片的新型图形转换器网络 (FGTN),用于预测人类生殖毒性.
将内部拓结构信息纳入QSAR建模中.
改进现有的基于原子的图形神经网络方法.

主要方法:

代表化合物作为图形,碎片作为节点,债券作为边缘.
引入超分子级节点来捕捉全球分子特征.
利用碎片嵌入和图形变压器架构进行预测.

主要成果:

在盲测试中,它以0.861的精度 (ACC) 和0.914的AUC实现了高性能.
性能优于传统的指纹模型和基于原子的图形卷积网络 (GCN) 模型.
证明了将毒性预测归因于特定碎片的能力,识别结构性警报.

结论:

FGTN模型提供了一个可靠和有效的工具,用于预测人类生殖毒性.
通过提供更高效,更具信息性的in silico方法,FGTN增强了化学品安全评估.
该模型能够识别有毒碎片,并可能区分异构体,这有助于推进计算毒理学.

关键词:

一个碎片的碎片.图形变压器网络网络的图形变压器网络.生殖性毒性生殖性毒性结构警报结构警报

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