New target prediction and visualization tools incorporating open source molecular fingerprints for TB Mobile 2.0

  • Alex M Clark, Malabika Sarker, Sean Ekins
  • Journal of Cheminformatics, August 2014, Springer Science + Business Media
  • DOI: 10.1186/s13321-014-0038-2

Major overhaul of the TB Mobile app and description of ECFP_6/FCFP_6 fingerprints in CDK project

What is it about?

The TB Mobile app is a free iPhone/iPad app to help scientists in their search for new cures for tuberculosis. It provides high quality curated data that is hard to find in the literature, and powerful cheminformatics tools wrapped up in an easy to use app. It has also implemented the ECFP_6 fingerprints for similarity comparisons, visual clustering and target prediction probabilities based on user-provided structures.

Why is it important?

The paper describes major improvements to the TB Mobile app, such as the target prediction and visual clustering. It also goes into significant detail describing the reference implementation of the ECFP_6/FCFP_6 descriptors, which have been made freely available as part of the Chemical Development Kit (CDK) library, and also implemented in the mobile app.


Dr Sean Ekins
Collaborations in Chemistry

Further development of the mobile app and validation. The addition of Bayesian models perhaps represents a useful example of how machine learning can be used in an app to make predictions.

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The following have contributed to this page: Dr Sean Ekins and Alex Michael Clark