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

  • Sean Ekins, Alex M. Clark, S. Joshua Swamidass, Nadia Litterman, Antony J. Williams
  • Journal of Computer-Aided Molecular Design, June 2014, Springer Science + Business Media
  • DOI: 10.1007/s10822-014-9762-y

Can drug discovery become more effective using open data and prediction tools?

What is it about?

We discuss how information from some drug discovery data sets 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.

Why is it important?

If more open data and access to the appropriate cheminformatics tools is made available to the relevant community then there is an opportunity to impact drug discovery, especially the efforts associated with the Open Drug Discovery community. A huge reason for this paper was the increasing growth in publically accessible chemistry and biology data and the need to use it and make sense of it.

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The following have contributed to this page: Dr Antony John Williams, Dr Sean Ekins, Alex Michael Clark, and Dr Nadia Litterman