## 1 Introduction
The PREDICT method combines various drug-drug and disease-disease similarities including chemical structure, predicted side effects, gene ontology, sequences, and phenotypes. The PREDICT [12] approach has been reported to obtain an area under the curve (AUC) for the receiver operating characteristic (ROC) of 0.92. In this strategy, the maximum score of the similarity scores was calculated for each drug-disease candidate pair. Another approach is similarity-based large-margin learning of multiple sources (SLAMS), which uses multiple data sources for candidate drugs, including chemical structures, protein targets, and side-effect profiles, to identify novel drugs for the treatment of diseases [13]. SLAMS can be used to demonstrate various characteristics of drugs and protein targets that play an important role in drug repositioning models, with a reported AUC of 0.89. This compares favorably to PREDICT, which can achieve an AUC of 0.87 when using integrated multiple data sources, while the prediction scores of the SLAMS model were higher than the PREDICT model by 32% and 59% for precision and recall, respectively. A similarity scheme that predicts approved and novel drug targets with new disease associations (SPANTD) has been used to investigate various interesting features, combining them in a scoring matrix. This scoring matrix includes protein similarities, common pathway sharing, binding sites, and disease similarities [14]. A genetic algorithm is employed on this scoring matrix to generate the prediction model that exhibits high performance (AUC = 0.97). The SPANTD model provided information on common pathways that are expressed as a connection between a drug and its targets. Therefore, the observation of the same action mechanisms can be used to suggest new drug targets for the repurposing of a drug. Most computational methods for predicting the drug repositioning are based on guilt-by-association using observations of similarities between drug-related proteins or genes in order to indicate for similar diseases. In particular, the type of relationship among genes, i.e., whether they are positive (e.g., activation) or negative (e.g., inhibition), is important information that has been proposed for use in modeling drug repositioning. This information calculates the relationship between genes based on their gene expression profiles, particularly the relationship between target genes and disease genes for drug repositioning [15]. However, the gene expression data for the interaction between target genes and disease genes are not always available, and these missing data can hamper model construction for drug repositioning. [PERSON] and [PERSON] [16] integrated existing gene networks from several databases, including BioCarta [17], Reactome [18], the Pathway Interaction Database (PID) [19], and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway [20], to calculate the shortest path score between drug-related genes (or target genes) and disease genes for both positive or negative interaction types [16]. Because several target genes can be influenced by a drug, the interaction types between drugs and target genes were determined to combine the shortest path scores for target genes that were activated by a disease gene for each drug. If the interaction type was positive (e.g., an activator, stimulator, or inducer), the shortest path score was multiplied by +1; otherwise, it was multiplied by \(-1\). Summing all of the shortest path scores expressed the value of a drug when associated with a disease gene. This technique suggested the assessment of model's performance in treating a disease with the drug. The objective of our research was to develop a drug repurposing approach that could be used to predict novel drugs for the treatment of a target disease using multiple biological characteristics summarized in protein-protein similarity vectors (PPSVs), which identify similarities between drug-related proteins and disease-related proteins.