CatNet:基于序列的深度学习与交叉注意力机制,用于识别内分泌干扰化学物质
Lu Zhao1, Qiao Xue2, Huazhou Zhang1
1State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, PR China; Sino-Danish College, University of Chinese Academy of Sciences, Beijing 100049, PR China.
Journal of hazardous materials
|November 28, 2023
概括
本研究介绍了CatNet,这是一种用于预测与核受体 (NR) 的化学相互作用的新框架. 它准确地识别了整个NR家族的内分泌干扰化学物质 (EDC),有助于环境和健康风险评估.
科学领域:
- 环境健康 环境健康
- 毒理学 毒理学 毒理学
- 计算生物学 计算生物学
背景情况:
- 内分泌干扰化学物质 (EDC) 通过干扰核受体 (NR) 干扰生理过程.
- 现有的研究往往忽视了整个NR家族的综合评估,专注于单个化合物-NR相互作用.
研究的目的:
- 开发一种用于评估各种化合物与人类核受体家族之间的相互作用的预测模型.
- 通过评估它们与多个NR的结合亲和力来识别潜在的内分泌干扰化学物质 (EDC).
主要方法:
- 汇集了 49,244 个化合物和 42 个人类 NR 之间的 35,467 个相互作用的综合数据集.
- 开发并应用了一个交叉注意力网络框架,CatNet,集成化合物和蛋白质特征.
- 利用特征可视化来获得对化合物-NR相互作用的机械洞察力.
主要成果:
- 在测试组中,CatNet实现了高预测性能,AUC_ROC为0.916.
- 该模型证明了对未见的复合NR对进行可靠的概括.
- 能够预测人类与NR相关的EDC,并可能识别新型EDC.
结论:
- CatNet提供了一种有效的计算方法,用于预测化合物-NR相互作用和识别EDC.
- 该框架能够评估整个NR家族,为化学风险提供了更全面的理解.
- 一个用户友好的图形界面促进了更广泛的研究应用.
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