Improving computational drug repositioning through multi-source disease similarity networks

Abstract

Computational drug repositioning seeks to identify new therapeutic uses for existing or experimental drugs. Network-based methods are effective as they integrate relationships among drugs, diseases, and target proteins/genes into prediction models. However, traditional approaches often rely on a single phenotype-based disease similarity network, limiting the diversity of disease information. In this study, we constructed three disease similarity networks-phenotypic, ontological, and molecular-using data from OMIM, Human Phenotype Ontology annotations, and gene interaction network, respectively. These were integrated into disease multiplex networks and multiplex-heterogeneous networks. We applied a tailored Random Walk with Restart (RWR) algorithm to predict novel drug-disease associations. Experimental results show that both disease multiplex and multiplex-heterogeneous networks outperform their single-layer counterparts in leave-one-out cross-validation. Using 10-fold cross-validation, our method, MHDR, outperformed the state-of-the-art methods TP-NRWRH, DDAGDL and RGLDR, demonstrating the advantage of integrating multiple disease similarity networks. We predicted novel drug-disease associations by ranking candidates, identifying 68 associations supported by shared proteins/genes, 1,064 by shared pathways, and 84 by shared protein complexes, with many validated by clinical trials, underscoring the practical impact of our approach.

Keywords: Disease multiplex networks; Drug repositioning; Multi-Source disease similarity networks; Multiplex-Heterogeneous networks; Random walk with restart (RWR).

Fig. 1

Network construction. First, single/monoplex drug/disease similarity networks are constructed, including: (a) DrSimNet_C, a drug similarity network constructed by computing the chemical structure-based similarity between two drugs; (b) DiSimNet_O, a disease similarity network constructed based on a disease vocabulary database, MeSH, which is used to annotate diseases in OMIM medical description records; (c) DiSimNet_H, a disease similarity network constructed using a disease annotation database, HPO, by a semantic similarity measure; and (d) DiSimNet_G, a disease similarity network built based on known associated genes and interactions in a gene network, HumanNet. Second, (e) a disease multiplex network is constructed using two or three monoplex disease similarity networks (e.g., DiSimNet_OHG). Finally, (f) a heterogeneous network of drugs and diseases is constructed by connecting a drug similarity network and a disease similarity network through known drug-disease associations (e.g., DrSimNet_C-DiSimNet_O), and (g) a multiplex-heterogeneous network is formed by connecting a drug similarity network with a disease multiplex network using known drug-disease associations (e.g., DrSimNet_C-DiSimNet_OHG).

Fig. 2

Prediction performance of the RWR-based methods on a disease multiplex network and a multiplex-heterogeneous network according to the change of (a) restart probability (), and (b) between-disease-disease-network jumping probability ().

Fig. 3

Performance comparison between single/monoplex disease similarity networks and multiplex disease networks. (a) AUROC curves for monoplex (DiSimNet_O, DiSimNet_H, DiSimNet_G) and multiplex (DiSimNet_OH, DiSimNet_OG, DiSimNet_HG, DiSimNet_OHG) networks. (b) AUPRC curves, highlighting performance on the imbalanced dataset (baseline AUPRC ~ 0.0007).

Fig. 4

Performance comparison between heterogeneous and multiplex-heterogeneous networks. (a) AUROC curves for networks with DrSimNet_P. (b) AUROC curves for networks with DrSimNet_C. (c) AUPRC curves for networks with DrSimNet_P. (d) AUPRC curves for networks with DrSimNet_C.

Fig. 5

Visualization of drug-disease associations via shared KEGG pathways and CORUM protein complexes, predicted by the DrSimNet_P-DiSimNet_OHG network. (a) The drug Estrone (KEGG ID: D00067) and breast cancer (MIMID: 114480) are linked by six shared pathways (e.g., estrogen signaling pathway, pathways in cancer) and two protein complexes (e.g., ER-alpha-p53-HDM2 complex, ESR1-MAGEA2-TP53 complex), supported by clinical trials like NCT01089049. (b) The drug Valsartan (KEGG ID: D00400) and Hypertension, Essential (MIMID: 145500) are connected by 14 shared pathways (e.g., renin-angiotensin system, neuroactive ligand-receptor interaction) and two protein complexes (e.g., AGTR1-AGTR2 complex, AGTR1-MAS1 complex), validated by trials such as NCT01878201. Diseases, drugs, pathways, and protein complexes are represented as nodes in blue squares, red circles, pink triangles, and purple diamonds, respectively. Green solid lines indicate associations, with edge weights reflecting the strength of connectivity between nodes.