State 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.
State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, PR China.
J Hazard Mater. 2024 Mar 5;465:133055. doi: 10.1016/j.jhazmat.2023.133055. Epub 2023 Nov 22.
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 (AUC) = 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.
内分泌干扰化学物质（EDCs）由于其潜在的干扰核受体（NRs）的能力，对环境和健康构成了重大风险，NRs 是生理过程的关键调节剂。尽管存在明显的风险，但大多数现有的研究都将重点缩小到化合物与单个 NR 靶标之间的相互作用上，而忽略了对整个 NR 家族的全面评估。有鉴于此，本研究构建了一个全面的人类 NR 数据集，其中包含 35467 种独特化合物与 42 种 NR 之间的 49244 种相互作用。我们引入了一个交叉注意网络框架“CatNet”，通过交叉注意机制创新地整合了化合物和蛋白质的表示。结果表明，CatNet 模型在测试集上的 AUC=0.916 表现出了优异的性能，并且在未见的化合物-NR 对上具有可靠的泛化能力。我们研究的一个显著特点是它能够扩展到新的目标。除了预测准确性之外，CatNet 还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，我们还开发了一个图形用户界面，使研究人员能够预测各种 NR 的化学结合。我们的模型能够预测与人类 NR 相关的 EDC，并显示出识别与其他靶标相关的 EDC 的潜力。
