Nuclear receptors (NRs) play a crucial role as biological targets in drug discovery. However, determining which compounds can act as endocrine disruptors and modulate the function of NRs with a reduced amount of candidate drugs is a challenging task. Moreover, the computational methods for NR-binding activity prediction mostly focus on a single receptor at a time, which may limit their effectiveness. Hence, the transfer of learned knowledge among multiple NRs can improve the performance of molecular predictors and lead to the development of more effective drugs. In this research, we integrate graph neural networks (GNNs) and Transformers to introduce a few-shot GNN-Transformer, Meta-GTNRP to predict the binding activity of compounds using the combined information of different NRs and identify potential NR-modulators with limited data. The Meta-GTNRP model captures the local information in graph-structured data and preserves the global-semantic structure of molecular graph embeddings for NR-binding activity prediction. Furthermore, a few-shot meta-learning approach is proposed to optimize model parameters for different NR-binding tasks and leverage the complementarity among multiple NR-specific tasks to predict binding activity of compounds for each NR with just a few labeled molecules. Experiments with a compound database containing annotations on the binding activity for 11 NRs shows that Meta-GTNRP outperforms other graph-based approaches. The data and code are available at: https://github.com/ltorres97/Meta-GTNRP . Scientific contribution The proposed few-shot GNN-Transformer model, Meta-GTNRP captures the local structure of molecular graphs and preserves the global-semantic information of graph embeddings to predict the NR-binding activity of compounds with limited available data; A few-shot meta-learning framework adapts model parameters across NR-specific tasks for different NRs in a joint learning procedure to predict the binding activity of compounds for each NR with just a few labeled molecules in highly imbalanced data scenarios; Meta-GTNRP is a data-efficient approach that combines the strengths of GNNs and Transformers to predict the NR-binding properties of compounds through an optimized meta-learning procedure and deliver robust results valuable to identify potential NR-based drug candidates.

中文翻译：

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结合图神经网络和变压器进行少样本核受体结合活性预测


核受体（NR）作为生物靶标在药物发现中发挥着至关重要的作用。然而，确定哪些化合物可以充当内分泌干扰物并通过减少候选药物的数量来调节 NR 的功能是一项具有挑战性的任务。此外，NR 结合活性预测的计算方法主要一次集中于单个受体，这可能会限制其有效性。因此，在多个 NR 之间转移学到的知识可以提高分子预测因子的性能，并导致更有效药物的开发。在这项研究中，我们集成了图神经网络（GNN）和 Transformer，引入了少样本 GNN-Transformer、Meta-GTNRP，利用不同 NR 的组合信息来预测化合物的结合活性，并利用有限的数据识别潜在的 NR 调节剂。 Meta-GTNRP 模型捕获图结构数据中的局部信息，并保留分子图嵌入的全局语义结构以用于 NR 结合活性预测。此外，还提出了一种少量元学习方法来优化不同 NR 结合任务的模型参数，并利用多个 NR 特定任务之间的互补性来预测每个 NR 化合物与少量标记分子的结合活性。对包含 11 种 NR 结合活性注释的化合物数据库进行的实验表明，Meta-GTNRP 优于其他基于图的方法。数据和代码可在以下网址获取：https://github.com/ltorres97/Meta-GTNRP。 科学贡献 提出的少样本 GNN-Transformer 模型 Meta-GTNRP 捕获分子图的局部结构并保留图嵌入的全局语义信息，以预测可用数据有限的化合物的 NR 结合活性；几次元学习框架在联合学习过程中针对不同的 NR 调整模型参数，以在高度不平衡的数据场景中仅用几个标记分子来预测每个 NR 的化合物的结合活性； Meta-GTNRP 是一种数据高效的方法，结合了 GNN 和 Transformer 的优势，通过优化的元学习程序来预测化合物的 NR 结合特性，并提供对识别潜在的基于 NR 的候选药物有价值的可靠结果。