标题： 图中图学习框架用于药物-靶点相互作用预测 显示英文标题

标题： A Graph-in-Graph Learning Framework for Drug-Target Interaction Prediction

摘要： 准确预测药物-靶点相互作用（DTIs）对于推进药物发现和靶点验证技术至关重要。 虽然基于图神经网络（GNN）的机器学习方法在DTI预测中取得了显著的成功，但其中许多方法在有效整合药物、靶点及其相互作用的多种特征方面存在困难。 为了解决这一限制，我们引入了一个新框架，利用归纳学习和直推学习的潜力，以便充分利用分子层面和药物-靶点相互作用网络层面的特征。 在这个框架中，有一个基于GNN的模型称为图中图（GiG），它将药物和靶点分子结构的图表示为药物-靶点相互作用图中的元节点，从而能够详细探索它们复杂的相互关系。 为了评估所提出的模型，我们整理了一个特殊的基准数据集，包括药物SMILES、蛋白质序列及其相互作用数据，这本身就很有趣。 我们的实验结果表明，GiG模型在所有评估指标上都显著优于现有方法，突显了整合不同学习范式和相互作用数据的优势。 显示英文摘要

摘要： Accurately predicting drug-target interactions (DTIs) is pivotal for advancing drug discovery and target validation techniques. While machine learning approaches including those that are based on Graph Neural Networks (GNN) have achieved notable success in DTI prediction, many of them have difficulties in effectively integrating the diverse features of drugs, targets and their interactions. To address this limitation, we introduce a novel framework to take advantage of the power of both transductive learning and inductive learning so that features at molecular level and drug-target interaction network level can be exploited. Within this framework is a GNN-based model called Graph-in-Graph (GiG) that represents graphs of drug and target molecular structures as meta-nodes in a drug-target interaction graph, enabling a detailed exploration of their intricate relationships. To evaluate the proposed model, we have compiled a special benchmark comprising drug SMILES, protein sequences, and their interaction data, which is interesting in its own right. Our experimental results demonstrate that the GiG model significantly outperforms existing approaches across all evaluation metrics, highlighting the benefits of integrating different learning paradigms and interaction data.