# Computational Drug Repositioning: Current Progress and Challenges
## 2 Overview of Drug Repositioning
### Network-Based Drug Repositioning  
Along with profile-based drug repositioning methods, a method using drug-target interaction has been widely used [50; 51; 52]. With the success of the Human Genome Project, many studies have been conducted to find genetic variants that affect common diseases using the data from genome-wide association studies (GWAS) [53; 54; 55; 56]. Drug repositioning studies using GWAS data often focus on whether proteins with disease-associated mutations will be the direct targets of drugs treating a disease.  
When the genes obtained from GWAS study are not the druggable targets, a network-based approach can provide the new candidate genes that have protein interactions with GWAS-associated genes. This approach combines information regarding drugs and their druggable targets obtained from various databases [10; 29; 57] and predicts potential drug candidates.  
However, these studies may be limited in their capacity to distinguish false positive genes from true positive genes, due to their linkage disequilibrium [53]. Some genes reveal fake disease associations from GWAS, as they are only co-located with disease-associated genes. Many studies have found new candidate drugs by combining gene and gene-drug networks or through expanding existing target genes using their biological pathways [53; 54; 55; 56]. When disease-related genes, identified through GWAS, do not act as targets of actual drugs, network-based methods expand them to a set of genes that will then be considered as new candidate drug targets. Network-based strategies construct drug-disease networks based on various data (e.g., gene expression, protein-protein interaction (PPI), GWAS) and identify potential candidate drugs [53; 58]. These interactions can be directly extracted from existing databases and studies or indirectly inferred using computational algorithms.  
Various network-based approaches have been applied to extend existing core disease-associated genes [46; 59; 60; 61; 62; 63; 64; 65; 66]. Network-based clustering algorithms were used to identify biological modules (also known as subnetworks) from drug-target and drug-disease networks based on the topological structures and the identified modules could be effectively utilized to select new potential candidate drug-target relationships [59; 60; 61; 62]. Vismodegib, an inhibitor of the Hedgehog signaling pathway, was identified to treat Gorlin syndrome [63] and iloperidone, an atypical antipsychotic for treatment of schizophrenia, was identified as the new potential drug for hypertension [60]. Network-based propagation approaches were also popularly applied to identify potential candidate drugs [47; 64; 65; 67]. Given a set of genes associated with a specific disease, they were extended to the genes sharing the neighborhoods in a PPI network or gene-gene network using a random walk propagation algorithm. Based on this approach, donepezil, methotrexate, gabapentin, risperidone, and cisplatin were also identified for new indications for [PERSON]'s disease, [PERSON]'s disease, anxiety disorder, obsessive-compulsive disorder, and breast cancer, respectively [67].