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J Cheminform. 2014 Aug 4;6:38. doi: 10.1186/s13321-014-0038-2. eCollection 2014.

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

Author information

1
Molecular Materials Informatics, 1900 St. Jacques #302, Montreal H3J 2S1, Quebec, Canada.
2
SRI International, 333 Ravenswood Avenue, Menlo Park 94025, CA, USA.
3
Collaborative Drug Discovery, 1633 Bayshore Highway, Suite 342, Burlingame 94010, CA, USA ; Collaborations in Chemistry, 5616 Hilltop Needmore Road, Fuquay-Varina 27526, NC, USA.

Abstract

BACKGROUND:

We recently developed a freely available mobile app (TB Mobile) for both iOS and Android platforms that displays Mycobacterium tuberculosis (Mtb) active molecule structures and their targets with links to associated data. The app was developed to make target information available to as large an audience as possible.

RESULTS:

We now report a major update of the iOS version of the app. This includes enhancements that use an implementation of ECFP_6 fingerprints that we have made open source. Using these fingerprints, the user can propose compounds with possible anti-TB activity, and view the compounds within a cluster landscape. Proposed compounds can also be compared to existing target data, using a näive Bayesian scoring system to rank probable targets. We have curated an additional 60 new compounds and their targets for Mtb and added these to the original set of 745 compounds. We have also curated 20 further compounds (many without targets in TB Mobile) to evaluate this version of the app with 805 compounds and associated targets.

CONCLUSIONS:

TB Mobile can now manage a small collection of compounds that can be imported from external sources, or exported by various means such as email or app-to-app inter-process communication. This means that TB Mobile can be used as a node within a growing ecosystem of mobile apps for cheminformatics. It can also cluster compounds and use internal algorithms to help identify potential targets based on molecular similarity. TB Mobile represents a valuable dataset, data-visualization aid and target prediction tool.

KEYWORDS:

Mobile app; Mycobacterium tuberculosis; TB mobile; Target prediction; Tuberculosis

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