KNDM: A Knowledge Graph Transformer and Node Category Sensitive Contrastive Learning Model for Drug and Microbe Association Prediction

It has been proven that the microbiome
in human bodies
can promote
or inhibit the treatment effects of the drugs by affecting their toxicities
and activities. Therefore, identifying drug-related microbes helps
in understanding how drugs exert their functions under the influence
of these microbes. Most recent methods for drug-related microbe prediction
are developed based on graph learning. However, those methods fail
to fully utilize the diverse characteristics of drug and microbe entities
from the perspective of a knowledge graph, as well as the contextual
relationships among multiple meta-paths from the meta-path perspective.
Moreover, previous methods overlook the consistency between the entity
features derived from the knowledge graph and the node semantic features
extracted from the meta-paths. To address these limitations, we propose
a knowledge-graph transformer and node category-sensitive
contrastive learning-based drug and microbe
association prediction model (KNDM). This model learns the diverse
features of drug and microbe entities, encodes the contextual relationships
across multiple meta-paths, and integrates the feature consistency.
First, we construct a knowledge graph consisting of drug and microbe
entities, which aids in revealing similarities and associations between
any two entities. Second, considering the heterogeneity of entities
in the knowledge graph, we propose an entity category-sensitive transformer
to integrate the diversity of multiple entity types and the various
relationships among them. Third, multiple meta-paths are constructed
to capture and embed the semantic relationships based on similarities
and associations among drug and microbe nodes. A meta-path semantic
feature learning strategy with recursive gating is proposed to capture
specific semantic features of individual meta-paths while fusing contextual
relationships among multiple meta-paths. Finally, we develop a node-category-sensitive
contrastive learning strategy to enhance the consistency between entity
features and node semantic features. Extensive experiments demonstrate
that KNDM outperforms eight state-of-the-art drug-microbe association
prediction models, while ablation studies validate the effectiveness
of its key innovations. Additionally, case studies on candidate microbes
associated with three drugs-curcumin, epigallocatechin gallate, and
ciprofloxacin-further showcase KNDM's capability to identify
potential
drug-microbe associations.