The discovery of drug-target interactions (DTIs) plays a crucial role in
pharmaceutical development. The deep learning model achieves more accurate
results in DTI prediction due to its ability to extract robust and expressive
features from drug and target chemical structures. However, existing deep
learning methods typically generate drug features via aggregating molecular
atom representations, ignoring the chemical properties carried by motifs, i.e.,
substructures of the molecular graph. The atom-drug double-level molecular
representation learning can not fully exploit structure information and fails
to interpret the DTI mechanism from the motif perspective. In addition,
sequential model-based target feature extraction either fuses limited
contextual information or requires expensive computational resources. To tackle
the above issues, we propose a hierarchical graph representation learning-based
DTI prediction method (HiGraphDTI). Specifically, HiGraphDTI learns
hierarchical drug representations from triple-level molecular graphs to
thoroughly exploit chemical information embedded in atoms, motifs, and
molecules. Then, an attentional feature fusion module incorporates information
from different receptive fields to extract expressive target features.Last, the
hierarchical attention mechanism identifies crucial molecular segments, which
offers complementary views for interpreting interaction mechanisms. The
experiment results not only demonstrate the superiority of HiGraphDTI to the
state-of-the-art methods, but also confirm the practical ability of our model
in interaction interpretation and new DTI discovery.

通过学习三层分子图中层次化的药物表征，结合关注机制，识别关键分子片段，该研究提出了一种基于层次化图表示学习的药物靶标相互作用预测方法，从而更好地利用原子、模式和分子中嵌入的化学信息，提取了表达丰富的靶标特征，展示出其在 DTI 预测上的优越性。

HiGraphDTI: 药物 - 靶标相互作用预测的分层图表示学习

HiGraphDTI: Hierarchical Graph Representation Learning for Drug-Target  Interaction Prediction

Graph pooling has been increasingly recognized as crucial for Graph Neural
Networks (GNNs) to facilitate hierarchical graph representation learning.
Existing graph pooling methods commonly consist of two stages: selecting
top-ranked nodes and discarding the remaining to construct coarsened graph
representations. However, this paper highlights two key issues with these
methods: 1) The process of selecting nodes to discard frequently employs
additional Graph Convolutional Networks or Multilayer Perceptrons, lacking a
thorough evaluation of each node's impact on the final graph representation and
subsequent prediction tasks. 2) Current graph pooling methods tend to directly
discard the noise segment (dropped) of the graph without accounting for the
latent information contained within these elements. To address the first issue,
we introduce a novel Graph Explicit Pooling (GrePool) method, which selects
nodes by explicitly leveraging the relationships between the nodes and final
representation vectors crucial for classification. The second issue is
addressed using an extended version of GrePool (i.e., GrePool+), which applies
a uniform loss on the discarded nodes. This addition is designed to augment the
training process and improve classification accuracy. Furthermore, we conduct
comprehensive experiments across 12 widely used datasets to validate our
proposed method's effectiveness, including the Open Graph Benchmark datasets.
Our experimental results uniformly demonstrate that GrePool outperforms 14
baseline methods for most datasets. Likewise, implementing GrePool+ enhances
GrePool's performance without incurring additional computational costs.

提出了一种新的图显式汇聚（GrePool）方法，通过明确利用节点之间的关系和最终表示向量的选择，解决了现有图汇聚方法中的两个关键问题，并进行广泛的实验证明其在分类准确度方面优于 14 个基线方法。

精心选择和深思熟虑的丢弃：利用被丢弃节点的图显式池化

Careful Selection and Thoughtful Discarding: Graph Explicit Pooling  Utilizing Discarded Nodes

Graph pooling that summaries the information in a large graph into a compact
form is essential in hierarchical graph representation learning. Existing graph
pooling methods either suffer from high computational complexity or cannot
capture the global dependencies between graphs before and after pooling. To
address the problems of existing graph pooling methods, we propose Coarsened
Graph Infomax Pooling (CGIPool) that maximizes the mutual information between
the input and the coarsened graph of each pooling layer to preserve graph-level
dependencies. To achieve mutual information neural maximization, we apply
contrastive learning and propose a self-attention-based algorithm for learning
positive and negative samples. Extensive experimental results on seven datasets
illustrate the superiority of CGIPool comparing to the state-of-the-art
methods.

提出了 Coarsened Graph Infomax Pooling (CGIPool) 方法，通过最大化每个池化层输入和粗化图之间的相互信息来维护图级依赖关系，并应用对比学习和提出自注意力算法来实现相互信息神经最大化。在七个数据集上的大量实验结果证明了 CGIPool 相对于现有的最先进方法的优越性。