Computational Approaches for Drug Repurposing

By Thomas Philpott

Due to the slow pace and substantial costs of new drug discovery and development, drug repurposing has become an attractive alternative.1 Drug repurposing is a strategy for identifying new uses for existing drugs, including approved, discontinued and investigational therapeutics.2 The COVID-19 pandemic has led to an urgency for developing new drugs to deal with SARS-CoV-2; as a result, repurposing has attracted further interest due to its potentially faster approach for finding a therapeutic. Molnupiravir was originally developed as a therapeutic for influenza but has since been repurposed against SARS-CoV-2.3 It is now the first oral antiviral for COVID-19 approved by the MHRA, demonstrating the power of drug repurposing.4

It currently takes 13-15 years and between $2-3 billion on average to get a new drug to market.5 Additionally, despite major advances in many of the scientific and technological inputs into drug discovery, the number of new drugs approved per billion dollars spent on research and development has roughly halved every 9 years since 1950.6 This trend has been named by Scannell et al.6 as Eroom’s law, which is Moore’s law spelt backwards. Drug repurposing offers a potentially less risky and more rapid return on investment. Estimates suggest that the costs of bringing a repurposed drug to market is 300 million dollars on average.5 Pushpakom et al.6 explain that drug repurposing offers several advantages over new drug discovery. The risk of failure is lower if the repurposed drug has already completed early-stage trials, the time taken for development can be reduced as most safely and preclinical testing will have been completed, and finally, less investment is needed (however, this varies depending on the stage of development of the repurposed candidate).

Historically, repurposed drugs have been identified serendipitously.7 They were found to have an off-target effect which was then exploited.1 Initial systematic approaches involved screening of chemical libraries against specific cell lines.8 More recently computational approaches have been employed thanks to growth of large-scale biomedical data such as microarray gene expression signatures and pharmaceutical databases along with high-performance computing.9 Most approaches are based on the idea that shared properties between compounds could hint at commonality in their mode of action.7 Examples include signature matching, molecular docking, pathway mapping and genetic association.

Signature mapping involves comparison of ‘signatures’ of a drug against that of another drug, disease or clinical phenotype.1 One such signature is differential gene expression. Lorio et al.7 explain that by comparing the expression profile of a cell or tissue before and after drug exposure, the signature of differential gene expression (SDE) can be determined. This is a summary of the drugs effects. If there is a sufficient negative correlation between a drug-induced SDE and a disease-associated SDE then it is reasonable to hypothesise that the effect of the drug on transcription is opposite to the effect of the disease and may be able to revert the disease-associated SDE. This relies on the signature reversion principle which assumes that if a drug can reverse the expression pattern that is the hallmark for a particular disease phenotype, it may be able to revert the disease phenotype.1 Alternatively, instead of comparing a drug-induced SDE to a disease-associated SDE, two drugs SDE could be compared. A shared SDE indicates that the two drugs could share a therapeutic application.7 Hsieh et al.10 successfully utilised this approach to identify GW8510, a cyclin-dependent kinase inhibitor as a potential inhibitor of RRM2. RRM2 is frequently overexpressed in colorectal cancer. They used the connectivity MAP (CMAP) database which collects gene expression profiles from small molecule treated cancer cells. By comparing the signatures this allowed connections among small molecules which could potentially share a therapeutic application be identified. They experimentally validated their computational results using western blot analysis which indicated that GW8510 inhibited RRM2 expression through promoting its proteasomal degradation.

An alternative approach is molecular docking which can be used to predict the bound conformations and free energies of binding for small molecule ligands into target receptors.11 The binding poses generated are then ranked using a scoring function.12 If there is prior knowledge of a receptor involved in a disease, the prediction of binding can be used to screen virtual libraries of drug-like molecules such as the ZINC database to identify leads for further drug development.13 In the context of redocking, instead of screening virtual libraries of drug-like molecules, existing drugs could be interrogated against the target receptor.1 AutoDock Vina is one of the fastest and most widely used open-source molecular docking programs which could be utilized for these virtual screens.14 Dakshanamurthy et al.15 used throughput molecular docking to screen 3671 FDA approved drugs against 2,335 protein crystal structures. They discovered that the antiparasitic mebendazole has the structural potential to inhibit VEGFR2, they then confirmed this experimentally. One key limitation with the molecular docking approach is that it requires the 3D structure of the target receptor to have been solved.

Whilst historically repurposed drugs have been identified serendipitously, computational approaches now provide a systematic and high throughput approach. There are already many publications detailing potential inhibitors which have been identified using these computational approaches. Repurposed drugs offer some key advantages over new drug discovery approaches, this has been shown during the COVID-19 pandemic with the success of Molnupiravir.


  1. Pushpakom S, Iorio F, Eyers P et al. Drug repurposing: progress, challenges and recommendations. Nat Rev Drug Discov. 2019;18: 41-58.
  2. Talevi A. Drug Repurposing. Reference Module in Biomedical Sciences. 2021.
  3. Imran M, Kumar Arora M, Asdaq SMB, et al. Discovery, Development, and Patent Trends on Molnupiravir: A Prospective Oral Treatment for COVID-19. Molecules. 2021;26(19):5795.
  4. Medicine and Healthcare products Regulatory Agency. First oral antiviral for COVID-19, Lagevrio (molnupiravir), approved by MHRA. [Accessed 29th January 2021]
  5. Nosengo N. Can you teach old drugs new tricks?. Nature. 2016;534: 314-316.
  6. Scannell J, Blanckley A, Boldon H, et al. Diagnosing the decline in pharmaceutical R&D efficiency. Nat Rev Drug Discov. 2012;11: 191-200.
  7. Iorio F, Rittman T, Ge H, Menden M, Saez-Rodriguez J. Transcriptional data: a new gateway to drug repositioning?. Drug Discov Today. 2013;18(7-8):350-357.
  8. Dudley JT, Deshpande T, Butte AJ. Exploiting drug-disease relationships for computational drug repositioning. Brief Bioinform. 2011;12(4):303-311.
  9. Jarada TN, Rokne JG, Alhajj R. A review of computational drug repositioning: strategies, approaches, opportunities, challenges, and directions. J Cheminform. 2020;12:46.
  10. Hsieh YY, Chou CJ, Lo HL, Yang PM. Repositioning of a cyclin-dependent kinase inhibitor GW8510 as a ribonucleotide reductase M2 inhibitor to treat human colorectal cancer. Cell Death Discov. 2016;9(2):16027.
  11. Forli S, Huey R, Pique M, et al. Computational protein–ligand docking and virtual drug screening with the AutoDock suite. Nat Protoc. 2016;11: 905-919.
  12. Kumar S, Kumar S. Chapter 6- Molecular Docking: A Structure-Based Approach for Drug Repurposing. In Silico Drug Design. 2019:161-189.
  13. Trott O, Olson AJ. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem. 2010;31(2):455-461.
  14. Eberhardt J, Santos-Martins D, Tillack AF, Forli S. AutoDock Vina 1.2.0: New Docking Methods, Expanded Force Field, and Python Bindings. Journal of Chemical Information and Modeling. 2021;61(8): 3891-3898.
  15. Dakshanamurthy S, Issa NT, Assefnia S, Seshasayee A, Peters OJ, Madhavan S, Uren A, Brown ML, Byers SW. Predicting new indications for approved drugs using a proteochemometric method. J Med Chem. 2012;55(15):6832-48.

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