Szydlik, S. | bioRxiv Science

bioRxiv2026

Beyond Chemical Similarity: Structure-Agnostic Drug-Drug Interaction Prediction with MeSH Semantics and a Drug-Target-Protein Knowledge Graph

BackgroundAdverse 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. ResultsDrugs 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 ({Delta}AUROC = 0.056;{Delta} F1 = 0.137) and Low-Mid interactions ({Delta}AUROC = 0.077;{Delta} F1 = 0.114). ConclusionsMeSH 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.

bioinformatics↗

bioRxiv2025

DTPPI: predicting drug interactions using a weighted drug-protein network

Polypharmacy, the practice of using multiple drugs to treat complex diseases, poses a significant risk of drug-drug interactions (DDIs), which can lead to unanticipated adverse drug reactions (ADRs) and toxicity. Identifying and understanding these DDIs is crucial to ensuring the safety of polypharmacy. Traditional laboratory-based methods for detecting DDI are costly and time consuming, prompting the development of computational approaches. However, many of these methods face limitations, mainly the lack of utilization of biological networks to model drug mechanics. Such an approach could lead to a new technique with better and more accurate DDI predictions. In response to these challenges, we propose the DTPPI network, a novel machine learning approach that leverages a drug-target-protein-protein interaction network to improve DDI prediction. By extracting topological features and combining them with biological drug features, the DTPPI method enhances the performance of a multilayer perceptron model. The evaluation results showed an AUC of 0.64 for topological characteristics alone, 0.89 for biological characteristics, and 0.91 for combined features, demonstrating that integrating topological and biological data significantly improves the prediction accuracy of DDI. Materials and implementations are available at: https://github.com/Golnazthr/DTPPI HighlightsO_LIConstructs a weighted graph network to model interactions among drugs, proteins, and targets, applicable to all drug types. C_LIO_LIExtracts six universal topological features from the graph, independent of chemical structure. C_LIO_LIEnhances DDI prediction by using topological features alone or alongside traditional drug features. C_LIO_LIIncorporates an MLP model for superior predictive accuracy using combined features. C_LI

systems biology↗