用图中图框架融合分子与相互作用数据，提升药物靶点预测精度
A Graph-in-Graph Learning Framework for Drug-Target Interaction Prediction
- 将药物和靶点分子结构表示为交互图中的元节点，构建图中图模型
- 在自定义基准上性能全面超越现有方法，准确率显著提升
- 适合药物发现、靶点验证领域研究者参考使用
准确预测药物-靶点相互作用（DTI）对推动药物发现和靶点验证至关重要。尽管基于图神经网络（GNN）的机器学习方法已在该任务中取得显著进展，但多数方法难以有效整合药物、靶点及其相互作用的多样化特征。为此，本文提出一种新框架，结合归纳学习与归纳学习的优势，充分挖掘分子层级与药物-靶点相互作用网络层级的特征。框架内设计了名为图中图（GiG）的GNN模型，将药物和靶点的分子结构图作为药物-靶点相互作用图中的元节点，实现对其复杂关系的精细建模。为评估模型，我们构建了一个包含药物SMILES、蛋白序列及相互作用数据的专项基准。实验结果表明，GiG模型在所有评估指标上均显著优于现有方法，验证了融合不同学习范式与交互数据的有效性。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。