Combining Cheminformatics Methods and Pathway Analysis to Identify Molecules with Whole-Cell Activity Against Mycobacterium Tuberculosis

  • Malabika Sarker, Carolyn Talcott, Peter Madrid, Sidharth Chopra, Barry A. Bunin, Gyanu Lamichhane, Joel S. Freundlich, Sean Ekins
  • Pharmaceutical Research, April 2012, Springer Science + Business Media
  • DOI: 10.1007/s11095-012-0741-5

Cheminformatics and pathways analysis for Mtb

What is it about?

We combined analysis using TBCyc and CDD to bring small pathway and molecule data together. We identified targets to focus on then used pharmacophore and machine learning to select compounds to test. We identified a couple of weak hits with whole cell activity predicted to mimic one target.

Why is it important?

The data collection alone on this project served as the foundation for the TB Mobile app. We provided a workflow for selection of targets, prioritizing, modeling and suggesting mimics as well as selecting molecules for testing and optimization. We made the collected data available in CDD TB.


Dr Sean Ekins
Collaborations in Chemistry

This work was the subject of an STTR and the mimic approach was greatly influenced by earlier work with Gyanu Lamichhane and Joel Freundlich. It became a valuable collaboration between several different labs. The work also provided the foundation for a later phase II grant.

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The following have contributed to this page: Dr Sean Ekins and Joel Freundlich