Journal of Computer-Aided Molecular Design

, Volume 28, Issue 10, pp 997–1008 | Cite as

Bigger data, collaborative tools and the future of predictive drug discovery

  • Sean EkinsEmail author
  • Alex M. Clark
  • S. Joshua Swamidass
  • Nadia Litterman
  • Antony J. Williams


Over the past decade we have seen a growth in the provision of chemistry data and cheminformatics tools as either free websites or software as a service commercial offerings. These have transformed how we find molecule-related data and use such tools in our research. There have also been efforts to improve collaboration between researchers either openly or through secure transactions using commercial tools. A major challenge in the future will be how such databases and software approaches handle larger amounts of data as it accumulates from high throughput screening and enables the user to draw insights, enable predictions and move projects forward. We now discuss how information from some drug discovery datasets can be made more accessible and how privacy of data should not overwhelm the desire to share it at an appropriate time with collaborators. We also discuss additional software tools that could be made available and provide our thoughts on the future of predictive drug discovery in this age of big data. We use some examples from our own research on neglected diseases, collaborations, mobile apps and algorithm development to illustrate these ideas.


Cloud Collaboration Cheminformatics Drug discovery Mobile apps 



S.E. gratefully acknowledges colleagues at CDD, Dr. Joel S. Freundlich (Rutgers), Dr. Malabika Sarker (SRI) and Dr. Katalin Nadassy (Accelrys) for valuable discussions and assistance in developing some of the projects discussed. S.E. acknowledges that the Bayesian models were developed with support from Award Number R43 LM011152-01 “Biocomputation across distributed private datasets to enhance drug discovery” from the National Library of Medicine. TB Mobile and the associated datasets was made possible with funding from Award Number 2R42AI088893-02 “Identification of novel therapeutics for tuberculosis combining cheminformatics, diverse databases and logic based pathway analysis” from the National Institutes of Allergy and Infectious Diseases.

Conflict of interest

N.L. is an employee and S.E. is a consultant for CDD Inc. S.E. is on the advisory board for Assay Depot. A.J.W. is an employee of the Royal Society of Chemistry. A.M.C. is the founder of Molecular Materials Informatics and a consultant for CDD.


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Sean Ekins
    • 1
    • 2
    Email author
  • Alex M. Clark
    • 3
  • S. Joshua Swamidass
    • 4
  • Nadia Litterman
    • 2
  • Antony J. Williams
    • 5
  1. 1.Collaborations in ChemistryFuquay-VarinaUSA
  2. 2.Collaborative Drug DiscoveryBurlingameUSA
  3. 3.Molecular Materials Informatics, Inc.MontrealCanada
  4. 4.Division of Laboratory and Genomic Medicine, Department of Pathology and ImmunologyWashington University School of MedicineSt. LouisUSA
  5. 5.Royal Society of ChemistryWake ForestUSA

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