# Computational Drug Repositioning: Current Progress and Challenges
## 2 Overview of Drug Repositioning
### Profile-Based Drug Repositioning  
Profile-based drug repositioning uses the bioactive profile of a drug in tandem with other drug or disease profiles. These profiles can be categorized as expression profiles, chemical structure profiles, or clinical profiles. An expression-based profile can be applied for drug-drug identification and drug-disease identification. Repositioned drugs reveal shared therapeutic mechanisms, which indicate their similar expression profile under different biological conditions, like cells or tissues. \begin{table}
\begin{tabular}{c c} \hline \hline
**Information** & **Database** \\ \hline \multirow{6}{*}{Chemical structure and drug’s activities} & PubChem \\  & CheEMBL \\  & DrugBank \\  & DrugCentral \\  & cMap \\  & STITCH \\ \hline \multirow{6}{*}{Transcriptional response induced by drugs} & Connectivity Map \\  & Library of Integrated Network-based Cellular \\  & Signatures (LINCS) \\ \hline \multirow{6}{*}{Protein structure} & Protein Data Bank (PDB) \\  & Protein Binding Sites (ProBis) \\  & Protein-Ligand Interaction Profiler (PLIP) \\ \hline \multirow{2}{*}{Protein structure and transcriptional profile and drug’s activities} & Drug Repurposing Hub \\  & Drug Target Commons \\  & Open Targets \\ \hline \multirow{2}{*}{Clinical information} & repoDB \\  & repurposeDB \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Resource for drug repositioning. Figure 2: The computational approaches for drug repositioning: profile-based drug repositioning, network-based drug repositioning, and data-based drug repositioning. These mechanisms allow us to identify candidate drugs and alternative targets of drugs. The comparison of transcriptomic profiles between drugs or between drugs and diseases can predict the hidden relationships between them. The expression profile of a drug shows the reverse expression profile of a specific target disease. This profile shows the dysregulated molecular expression caused by a disease, which can potentially be recovered by a drug that has the opposite transcriptomic profile. Put simply, the over-expressed genes in the diseased condition can be treated with the drug, inducing downregulation of those genes. The principle of this computational strategy is simple, yet incredibly effective at identifying drugs with compatible expression profiles similar to a healthy state as opposed to a diseased state. A strong, negative, correlation between the transcriptomic expression profiles of a drug and a disease would show the potential effect on the disease of a drug. The Connectivity Map (cMap), developed by the Broad Institute, is a publicly accessible gene expression database generated through the results of dosing multiple cell types with more than 1300 compounds [17]. This resource can be combined with other public repositories of gene expression data such as those described in Table 2, to make cMap more effective. The complete profiles obtained from these databases can be used to identify novel drug-disease relationships and potential candidate drugs. The expression profile-based approach has discovered the new indication of phenoxybenzamine, an anti-hypertensive drug, as an analgesic and antinociceptive drug [35]. The second type of profile-based strategy is using chemical structure techniques, such as molecular docking [36; 37; 38; 39; 40; 41; 42; 43; 44]. Similar chemical structures lead to the similar biological activities [45]. Potential candidate drugs, predicted by structural similarity with target molecules, are used to identify new drug-target associations. The similarity of the ligand binding site is of particular use in identifying potential drug-disease pairs [40; 41; 46; 47]. For example, if a specific protein, which is a causative target of disease A, has a similar local structure with that of protein B, which is a therapeutic target of drug B, we can predict that drug B may be used for disease A. This simple strategy is effective in drug repositioning.