# Computational Drug Repositioning: Current Progress and Challenges
## 2 Overview of Drug Repositioning  
Drug repositioning methods can be broadly divided into computational and experimental approaches. Sometimes, these methods are combined to utilize the benefit from both computational methods and experimental screenings. Furthermore, studies to find more universally successful candidate drugs have progressed by combining various types of data, such as genomic data, proteomic data, clinical health data, and literature data.  
Computational approaches heavily rely on available data, which are crucial in predicting new indications of existing drugs through the utilization of systematic drug-target-disease relationship combinations. As technology advanced, newly discovered drug-target-disease information began to  
\begin{table}
\begin{tabular}{c c c} \hline
**Drug** & **Original Indication** & **New Indication** \\ \hline Aspirin & Inflammation, analgesia & Antiplatelet, colorectal cancer \\ Atomoxetine & Parkinson disease, depressive disorder & ADHD \\ Amandatine & Influenza & Parkinson’s disease \\ Amphotericin & Fungal infections & Antiplatelet \\ Allopurinol & Cancer & Gout \\ Arsenic & Syphilis & Leukemia \\ Bromcoripine & Parkinson’s disease & Diabetes mellitus \\ Burropion & Depression & Smoking cessation \\ Bimatoprost & Glaucoma & Promoting eyelash growth \\ Celecoxb & Pain and inflammation & Familial adenomatous polyps \\ Colesevelam & Hyperlipidemia & Type 2 diabetes mellitus \\ Colchicine & Gout & Recurrent pericarditis \\ Duloxetine & Depression & Stress urinary incontinence \\ Disulfram & Alcoholism & Melanoma \\ Dapsone & Leprosy & Malaria \\ Doxepin & Depressive disorder & Antipruritic \\ Dapoxetine & Analgesia and depression & Premature ejaculation \\ Elformithine & Depression & ADHD \\ Engelomlod & Transplant rejection & Multiple sclerosis \\ Gabapentin & Epilepsy & neuropathic pain \\ Gemcitabine & Antiviral & Cancer \\ Ketoconazole & Fungal infections & Cushing syndrome \\ Lomitapide & Lipidemia & Familial hypercholesterolemiaemia \\ Methotrexate & Cancer & Psoriasis, rheumatoid arthritis \\ Miltefosine & Cancer & Visceral leishmaniasis \\ Minoxidil & Hypertension & Hair loss \\ Naltrexone & Opioid addiction & Alcohol withdrawal \\ Naproxen & Inflammation, pain & Alzheimer’s disease \\ Norrtptyline & Depression & Neuropathic pain \\ Propranolol & Hypertension & Migraine prophylaxis \\ Pemetreved & Mesothelioma & Lung cancer \\ Retinoic acid & Acne & Acute promylocytic leukemia \\ Roppirinrole & Parkinson’s disease & Restless leg syndrome \\ Rituximab & Various cancers & Rheumatid arthritis \\ Raloxifene & Osteoporosis, contraceptive & Breast cancer, osteoporosis \\ Siladenafil & Angina & Erectile dysfunction, pulmonary hypertension \\ Topiramate & Epilepsy & Obesity \\ Thalidomide & Morning sickness & Leprosy, multiple myeloma \\ Tretinoin & Acne & Leukemia \\ Zidovudine & Cancer & HIV/AIDS \\ Zileuton & Asthma & Acne \\ \hline \end{tabular}
\end{table}
Table 1: Repositioned drug list.  
accumulate within various databases, such as DrugBank [10], ChemBank [11], Genecard [12], OMIM [13], and PubMed (Table 2). The accumulation of various data (e.g., gene expression, chemical structure, proteomic data, literature data, and electronic health records [EHR]) allows for the development of novel methods for drug repositioning by highlighting potential candidates for drug-disease relationships [14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30]. Several comprehensive reviews have been done to present the overall strategies and challenges in drug repositionings by focusing on specific perspectives [31; 32; 33; 34]. These computational approaches to drug repositioning can be divided into the three strategies: profile-based drug repositioning, network-based drug repositioning, and data-based drug repositioning (Figure 2).