CatNet: Sequence-based deep learning with cross-attention mechanism for identifying endocrine-disrupting chemicals.
CatNet: Sequence-based deep learning with cross-attention mechanism for identifying endocrine-disrupting chemicals.
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DOI：
10.1016/j.jhazmat.2023.133055
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发表时间：
2023-11
影响因子：
13.6
通讯作者：
Lu Zhao;Q. Xue;Huazhou Zhang;Yuxing Hao;Hang Yi;Xian Liu;Wenxiao Pan;Jianjie Fu;Aiqian Zhang
中科院分区：
文献类型：
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作者：
Lu Zhao;Q. Xue;Huazhou Zhang;Yuxing Hao;Hang Yi;Xian Liu;Wenxiao Pan;Jianjie Fu;Aiqian Zhang
内分泌干扰化学物质 (EDC) 可能会干扰生理过程的关键调节因子核受体 (NR)，从而带来重大的环境和健康风险。尽管存在明显的风险，但大多数现有研究都将重点缩小到化合物与单个 NR 目标之间的相互作用，而忽略了对整个 NR 家族的全面评估。为此，本研究构建了一个全面的人类 NR 数据集，捕获了 35,467 种独特化合物和 42 种 NR 之间的 49,244 种相互作用。我们引入了交叉注意力网络框架“CatNet”，通过交叉注意力机制创新性地整合了化合物和蛋白质表示。结果表明，CatNet 模型在测试集上取得了优异的性能，受试者工作特征曲线下面积 (AUCROC) = 0.916，并且对未见过的化合物-NR 对表现出可靠的泛化能力。我们研究的一个显着特点是其扩展到新目标的能力。除了预测准确性之外，CatNet 通过特征可视化提供了关于化合物-NR 相互作用的有价值的机制视角。为了增强我们研究的实用性，我们还开发了图形用户界面，使研究人员能够预测与不同 NR 的化学结合。我们的模型能够预测人类 NR 相关的 EDC，并显示出识别与其他目标相关的 EDC 的潜力。
Endocrine-disrupting chemicals (EDCs) pose significant environmental and health risks due to their potential to interfere with nuclear receptors (NRs), key regulators of physiological processes. Despite the evident risks, the majority of existing research narrows its focus on the interaction between compounds and the individual NR target, neglecting a comprehensive assessment across the entire NR family. In response, this study assembled a comprehensive human NR dataset, capturing 49,244 interactions between 35,467 unique compounds and 42 NRs. We introduced a cross-attention network framework, "CatNet", innovatively integrating compound and protein representations through cross-attention mechanisms. The results showed that CatNet model achieved excellent performance with an area under the receiver operating characteristic curve (AUCROC) = 0.916 on the test set, and exhibited reliable generalization on unseen compound-NR pairs. A distinguishing feature of our research is its capacity to expand to novel targets. Beyond its predictive accuracy, CatNet offers a valuable mechanistic perspective on compound-NR interactions through feature visualization. Augmenting the utility of our research, we have also developed a graphical user interface, empowering researchers to predict chemical binding to diverse NRs. Our model enables the prediction of human NR-related EDCs and shows the potential to identify EDCs related to other targets.