Introduction In order for us to learn from the extensive prior literature we have collated information on molecules screened versus Mycobacterium tuberculosis (Mtb) and their predominantly experimentally confirmed targets which has been made available in the Collaborative Drug Discovery (CDD) database. This dataset contains data on target, essentiality, links to PubMed, TBDB, TBCyc and human homolog information. The development of mobile cheminformatics apps could potentially lower the barrier to drug discovery and promote collaboration. Therefore we have used this set of over 700 molecules screened versus Mtb and their targets to create a free mobile app (TB Mobile) that displays molecule structure and links to the bioinformatics data. By input of the molecule structure the user can perform a similarity search within the app and infer potential targets. In addition one can search by targets to retrieve compounds known to be active. Methods The development of the TB mobile is described elsewhere (Ekins et al.,submitted), we now focus on evaluation of a recent dataset of 11 compounds that came out of whole cell screening by GSK (Ballell et al 2013, in press) to demonstrate how the app can be used. The workflow used is as follows: First the 11 molecules were drawn in the MMDSapp and exported into the TB Mobile app (an example of app-to-app communication, Figure 1). The similarity searching Using TB Mobile to Predict Potential Targets for TB hits from Phenotypic Screening Sean Ekins 1,2 and Alex M. Clark 3 1 Collaborative Drug Discovery, 1633 Bayshore Hwy, Suite 342, Burlingame, CA 94010, 2 Collaborations in Chemistry, 5616 Hilltop Needmore Road, Fuquay Varina, NC 27526, U.S.A., 2 Molecular Materials Informatics, 1900 St. Jacques #302, Montreal, Quebec, Canada H3J 2S1., (S. Ekins can be contacted at Figure 1. Workflow from sketching molecules in MMDS mobile app to exporting and opening with TB Mobile Figure 2. screenshots of similarity searching in TB mobile for each of the GSK molecules in the Ballel et al 2013 paper. Table 1. Predictions for GSK compounds from phenotypic Mtb screening component was used to rank the content in TB Mobile of molecules with known targets. We have used this as an example of inferring potential targets. The molecules are ranked and labelled with their target/s. It should be noted that such data is far from definitive as these published compounds have not been tested versus all Mtb targets and it is possible the same compound may be active against more than one target. Results Figure 2 and Table 1 suggest the 11 hits from GSK may be targeting a limited array of targets. This could be due to limitation of the underlying data in TB Mobile biased towards those with larger numbers of molecules. Some molecules have features one sees repeatedly e.g. GSK353069A looks like a dhfr inhibitor. We have not performed any experimental verification of these predictions and present our observations openly to demonstrate the potential utility of TB Mobile and foster collaboration. Compound availability is however unclear. References L. Ballell, R. H. Bates, R. J. Young, D. Alvarez-Gomez, E. Alvarez-Ruiz, V. Barroso, D. Blanco, B. Crespo, J. Escribano, R. Gonzalez, S. Lozano, S. Huss, A. Santos-Villarejo, J. J. Martin-Plaza, A. Mendoza, M. J. Rebollo-Lopez, M. Remuinan-Blanco, J. L. Lavandera, E. Perez-Herran, F. J. Gamo-Benito, J. F. Garcia-Bustos, D. Barros, J. P. Castro, N. Cammack, Fueling Open-Source drug discovery: 177 small-molecule leads against tuberculosis ChemMedChem Data sources TB Mobile for iOS mobile/id ?mt=8https://itunes.apple.com/us/app/tb- mobile/id ?mt=8 TB Mobile for Android ?id=com.mmi.android.tbmobile ?id=com.mmi.android.tbmobile CDD