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Computational analysis of novel drug opportunities (CANDO)

Find a new cure or treatment for a neglected disease with us!

Computational multidisease multitarget screening pipeline. Follow the link at right for a detailed description.



We have a developed a novel and unique computational multitarget fragment-based docking with dynamics protocol to implement a comprehensive and efficient drug discovery pipeline with higher efficiency, lowered cost, and increased success rates, compared to current approaches.

We will apply this pipeline to evaluate how all approved drugs bind to all known disease target protein structures. The top predictions are verified in the laboratory and clinic to repurpose drugs approved for other indications as new therapeutics, particularly for underserved diseases.

The project represents an integration of our group's applied research on therapeutic discovery, building upon basic protein structure, function, and interaction prediction research. Funding sources include the 2010 NIH Director's Pioneer Award, the University of Washington Technology Gap Innovation Fund, and the Washington Research Foundation.

The 2010 NIH Director's Pioneer Award application





Collaboration email questionnaire Please fill and email to us if you're interested in working with us on your favourite indication(s). We will endeavour to reply as soon as possible.


Drug discovery is protein folding with a compound.

  • Docking with protein structure + ligand dynamics.
  • Multitargeting.
  • Automated binding site identification.
  • Can be used to computationally assess new compounds from combinations of fragments (+).
  • Using all the known information about current drug and drug like compounds.
  • Learning from affinity measures separating entropy and enthalpy.
  • Predict toxicity through nonspecific binding.
  • Predict ligand-target networks.
  • Fragmentation of drugs to identify pharmacophore.


  • Drug profiles across multiple targets (not single drug per target paradigm).
  • Molecular and systems level integration because of drug profile (i.e., how each compound interacts with the interactome).
  • Exploiting the fact that all drug discovery thusfar has been a feature of Evolution.
  • Consolidates almost all one off inhibitor discovery in one shotgun approach.
  • Systems based drug discovery.
  • New compounds (+) predicted to be nontoxic can be used to explore beyond the CANDO space for very intractable diseases.
  • If successful, it will move compbio frameworks forward unlike never before.


Ultimately the goal is personalisation to improve quality of life, including personalised medicine. When I first came across genetics, my dream was that each person would have their genome sequence and a powerful computing cluster (these days, one can get a personal supercomputer for ~$6000) where they could evaluate the response of their proteins and proteomes (corresponding to their specific genes and genomes) against entities in the environment, such as bioactive chemical compounds, to improve their quality of life, i.e., to treat and/or cure diseases as well develop vaccines. This project is part of that dream and we're going to rigourously evaluate whether it can come to fruition. Indications and collaborators


Cure or treat   by inhibiting                  verified by 	           at
Dengue          Dengue virus                   Scott Michael               Florida Gulf Coast University, USA
Herpes          HSV, CMV, KHSV, EBV            Michael Lagunoff            University of Washington, Seattle, USA
Malaria         P. falciparum                  Wesley van Voorhis          University of Washington, Seattle, USA
_                                              Greg Cowther                University of Washington, Seattle, USA
Hepatitis C     Hepatitis C virus              Lorne Tyrell                University of Alberta, Edmonton, Canada
CFS             XMRV                           Andrew Mason 	           University of Alberta, Edmonton, Canada
PBC             HBRV                           Andrew Mason 	           University of Alberta, Edmonton, Canada
_                                              Gane Wong                   University of Alberta, Edmonton, Canada
Thalassemia 		                       Surakameth Mahasirimongkol  National Institute of Health, Bangkok, Thailand
HIV 		                               Wasun Chantratita           Ramathibodi Hospital + Mahidol University, Bangkok, Thailand
Cholangiocarcinoma 		               Natini Jinawath             Ramathibodi Hospital + Mahidol University, Bangkok, Thailand
Prostate cancer 		               Soranan Chantarangsu        Ramathibodi Hospital + Mahidol University, Bangkok, Thailand
Dental caries   S.mutans, S.sobrinus, L.casei  Jeremy Horst 	           University of California, San Francisco, USA

This section is much in progress. If our collaboration isn't listed, then we're sorry and it will be soon!


  • Ram Samudrala (pion)
  • Brian Buttrick (in virtuale multitargeting shotgun drug discovery pipeline, and beyond)
  • Gaurav Chopra (fragment based docking with dynamics)
  • Michael Shannon (systems and database administrator/programmer)
  • Michael Zhou (dengue, scoring function)
  • Brady Bernard (all around consultant, 3dtherapeutics, commercialisation)
  • David Beck (all around consultant)
  • Ling-Hong Hung (aide)
  • Jeremy Horst



  • Ekachai Jenwitheesuk (docking with dynamics, herpes, malaria, dengue)


  • US NIH Director's Pioneer Award (2010-2015).
  • US NSF CAREER Award IIS-0448502 (2005-2010).
  • US NIH F30DE017522 (2006-2010).
  • The University of Washington's Technology Gap Innovation Fund (2006-2007).
  • Washington Research Foundation (2006-2007).
  • Puget Sound Partners in Global Health (2004-2005).
  • Searle Scholar Award to Ram Samudrala (2002-2005).
  • The University of Washington's Advanced Technology Initiative in Infectious Diseases (2001-).


Conceptual Publications

Primary Publications

  1. Costin JM, Jenwitheesuk E, Lok S-M, Hunsperger E, Conrads KA, Fontaine KA, Rees CR, Rossmann MG, Isern S, Samudrala R, Michael SF. Structural optimization and de novo design of dengue virus entry inhibitory peptides. PLoS Neglected Tropical Diseases 4: e721, 2010.
  2. Movahedzadeh F, Balaubramanian V, Bernard B, Iyer S, Samudrala R, Franzblau SG, Balganesh TS. Anti-tuberculosis agents: A rational approach for discovery and development. Genomic and computational tools for emerging infectious diseases, 2010. in press.
  3. Bernard B, Samudrala R. A generalized knowledge-based discriminatory function for biomolecular interactions. Proteins: Structure, Function, and Bioinformatics 76: 115-128, 2009.
  4. Jenwitheesuk E, Horst JA, Rivas K, Van Voorhis WC, Samudrala R. New paradigms for drug discovery: Computational multitarget screening. Trends in Pharmacological Sciences 29: 62-71, 2008.
  5. Wang K, Mittler J, Samudrala R. Comment on "Evidence for positive epistatis in HIV-1". Science 312: 848b, 2006.
  6. Jenwitheesuk E, Samudrala R. Identification of potential multitarget antimalarial drugs. Journal of the American Medical Association 294: 1490-1491, 2005.
  7. Samudrala R, Jenwitheesuk E. Identification of potential HIV-1 targets of minocycline. Bioinformatics 23: 2797-2799, 2007.
  8. Jenwitheesuk E, Samudrala R. Heptad-repeat-2 mutations enhance the stability of the enfuvirtide-resistant HIV-1 gp41 hairpin structure. Antiviral Therapy 10: 893-900, 2005.
  9. Jenwitheesuk E, Wang K, Mittler J, Samudrala R. PIRSpred: A webserver for reliable HIV-1 protein-inhibitor resistance/susceptibility prediction. Trends in Microbiology 13: 150-151, 2005.
  10. Jenwitheesuk E, Samudrala R. Virtual screening of HIV-1 protease inhibitors against human cytomegalovirus protease using docking and molecular dynamics. AIDS 19: 529-533, 2005.
  11. Jenwitheesuk E, Samudrala R. Prediction of HIV-1 protease inhibitor resistance using a protein-inhibitor flexible docking approach. Antiviral Therapy 10: 157-166, 2005.
  12. Jenwitheesuk E, Wang K, Mittler J, Samudrala R. Improved accuracy of HIV-1 genotypic susceptibility interpretation using a consensus approach. AIDS 18: 1858-1859, 2004.
  13. Jenwitheesuk E, Samudrala R. Identifying inhibitors of the SARS coronavirus proteinase. Bioorganic & Medicinal Chemistry Letters, 13: 3989-3992, 2003.
  14. Jenwitheesuk E, Samudrala R. Improved prediction of HIV-1 protease-inhibitor binding energies by molecular dynamics simulations. BMC Structural Biology 3: 2, 2003.
  15. Wang K, Jenwitheesuk E, Samudrala R, Mittler J. Simple linear model provides highly accurate genotypic predictions of HIV-1 drug resistance. Antiviral Therapy 9: 343-352, 2004.
  16. Wang K, Samudrala R, Mittler J. Weak agreement between predictions of ``reduced susceptibility from Antivirogram and PhenoSense assays. Journal of Clinical Microbiology 42: 2353-2354, 2004.
  17. Wang K, Samudrala R, Mittler J. HIV-1 genotypic drug resistance interpretation algorithms need to include hypersusceptibility mutations. Journal of Infectious Diseases 190: 2055-2056, 2004.
  18. Wang K, Samudrala R, Mittler J. Antivirogram or PhenoSense: a comparison of their reproducibility and an analysis of their correlation. Antiviral Therapy 9: 703-712, 2004.


UWCompBio research All our publications.


Samudrala Computational Biology Research Group

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