InteractionNet：基于非共价图神经网络和分层关联传播的蛋白质与配体相互作用的建模与解释
InteractionNet: Modeling and Explaining of Noncovalent Protein-Ligand Interactions with Noncovalent Graph Neural Network and Layer-Wise Relevance Propagation
摘要：
将基于图的深度学习模型的范围扩展到非共价蛋白质与配体相互作用，在基于结构的药物设计中赢得了越来越多的关注。用图神经网络（GNN）对蛋白质与配体相互作用进行建模在将蛋白质-配体复合物结构转换为图表示方面遇到了困难，并且留下了关于训练的模型是否正确地学习适当的非共价相互作用的问题。在这里，我们提出了一个GNN架构，表示为InteractionNet，它通过不同的卷积层学习两个独立的分子图，共价和非共价。我们还使用可解释性技术分析了InteractionNet模型，即，逐层相关性传播，用于检查模型预测的化学相关性。共价和非共价卷积步骤的分离使得可以独立地评估每个步骤的贡献并分析非共价相互作用的图形构建策略。我们将InteractionNet应用于蛋白质-配体结合亲和力的预测，并表明我们的模型成功地预测了非共价相互作用的性能和化学解释的相关性。
英文摘要：
Expanding the scope of graph-based, deep-learning models to noncovalent protein-ligand interactions has earned increasing attention in structure-based drug design. Modeling the protein-ligand interactions with graph neural networks (GNNs) has experienced difficulties in the conversion of protein-ligand complex structures into the graph representation and left questions regarding whether the trained models properly learn the appropriate noncovalent interactions. Here, we proposed a GNN architecture, denoted as InteractionNet, which learns two separated molecular graphs, being covalent and noncovalent, through distinct convolution layers. We also analyzed the InteractionNet model with an explainability technique, i.e., layer-wise relevance propagation, for examination of the chemical relevance of the model's predictions. Separation of the covalent and noncovalent convolutional steps made it possible to evaluate the contribution of each step independently and analyze the graph-building strategy for noncovalent interactions. We applied InteractionNet to the prediction of protein-ligand binding affinity and showed that our model successfully predicted the noncovalent interactions in both performance and relevance in chemical interpretation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
