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

Sean Ekins, Alex M. Clark, S. Joshua Swamidass, Nadia Litterman, Antony J. Williams

Research output: Contribution to journalArticlepeer-review

21 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)997-1008
Number of pages12
JournalJournal of Computer-Aided Molecular Design
Volume28
Issue number10
DOIs
StatePublished - Jun 19 2014

Keywords

  • Cheminformatics
  • Cloud
  • Collaboration
  • Drug discovery
  • Mobile apps

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