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

• Published in: Journal of cheminformatics Vol. 6; no. 1; p. 38
• Main Authors: Clark, Alex M, Sarker, Malabika, Ekins, Sean
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 04.08.2014; Springer Nature B.V; BioMed Central
• Subjects: Binding sites; Chemistry; Chemistry and Materials Science; Computational Biology/Bioinformatics; Computer Applications in Chemistry; Documentation and Information in Chemistry; Drug resistance; Methods; Mycobacterium tuberculosis; Pharmaceutical industry; Research Article; Theoretical and Computational Chemistry; Tuberculosis; Tuberculosis; Mycobacterium tuberculosis; Mobile app; TB mobile; Target prediction
• ISSN: 1758-2946; 1758-2946
• DOI: 10.1186/s13321-014-0038-2

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.