Beyond Chemical Similarity: Structure-Agnostic Drug–Drug Interaction Prediction with MeSH Semantics and a Drug–Target–Protein Knowledge Graph
TL;DR
A structure-agnostic framework that combines semantic representations derived from Medical Subject Headings with graph-derived topology from a Drug–Target–Protein knowledge graph constructed from DrugBank and UniProt is presented and it is found that graph-derived topology is particularly beneficial when ontology annotations are shallow.
Abstract
Background Adverse drug–drug interactions (DDIs) cause preventable hospitalizations, but exhaustive experimental screening of all drug pairs is infeasible. Many computational predictors rely on SMILES or other molecular representations, limiting their direct applicability to biologics and other non-small-molecule therapeutics. We present a structure-agnostic framework that combines semantic representations derived from Medical Subject Headings (MeSH) with graph-derived topology from a Drug–Target–Protein knowledge graph constructed from DrugBank and UniProt. We further investigate how variation in MeSH annotation depth affects predictive performance. Results Drugs are grouped according to their deepest MeSH annotation level (Low, Mid, or Deep), and performance is evaluated across the resulting interaction categories in transductive and inductive settings. The Intermediate ontology scope (Low+Mid) provides the most stable performance, while adding Deep-level terms offers limited and inconsistent benefit. Lightweight topological descriptors are integrated with MeSH features through instance-wise, dimension-specific latent-space gating, using curated reliable-negative pairs for supervision. Fusion improves mean performance over the MeSH-only baseline across all six categories in the transductive setting. Under induction, the clearest gains occur for Low–Low interactions (ΔAUROC = 0.056; ΔF1 = 0.137) and Low–Mid interactions (ΔAUROC = 0.077; ΔF1 = 0.114). Conclusions MeSH annotation depth is associated with systematic variation in DDI prediction performance that aggregate evaluation can obscure. Graph-derived topology is particularly beneficial when ontology annotations are shallow. The framework provides a common, structure-agnostic representation compatible with both small-molecule and biologic therapeutics and supports first-pass DDI prioritization for subsequent expert assessment.