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Freundlich Lab

Our Mission and What Motivates Us

The Freundlich group is a chemical biology lab that utilizes a multi-disciplinary approach to study infectious diseases, with a specific focus on tuberculosis. Within the Department of Pharmacology & Physiology and the Department of Medicine (Division of Infectious Diseases, Center for Emerging & Re-emerging Pathogens), we focus on 1) the development and application of novel computational methods to discover and optimize chemical probes with which to study the pathogenesis of disease and 2) the utilization of biological techniques to elucidate how each probe affects disease pathogenesis, often through the complex modulation of more than one biological target. We assert that this approach has significant potential to seed the discovery of novel biological targets and small molecule drug discovery hits/leads.


Photo of the members of the Freundlich lab, taken Jan. 2014



8/20/2014 – The website for our NIH-funded CETR is now live. Please visit

8/01/2014 – Congratulations to Tom Stratton for giving a great presentation at the successful completion of the Graduate School of Biomedical Sciences Undergraduate Summer Research Fellowship, which funded his research for the last few months.

8/01/2014 – Congratulations to Alex Perryman and Xin Wang on their paper with the Kozikowski, Jacobs, and Bishai groups on novel inhibitors of InhA, which was just accepted in ChemMedChem. Our contribution was performing molecular modeling, enzyme biochemistry and inhibition assays of the new small molecule inhibitors.

7/21/2014 – Congratulations to Tom Stratton, who was recently distinguished as an Anna and Bernard Senkowski Undergraduate Chemistry Scholar. 

7/14/2014 – Congratulations to Alex for updating IBM's World Community Grid volunteers on promising results from GO FAM against malaria and tuberculosis.

7/01/2014 – Congratulations to Joel on his promotion to Associate Professor!

6/26/2014 – Congratulations to the group on the just accepted manuscript at the Journal of Chemical Information and Modeling entitled "Are Bigger Datasets Better for Machine Learning? Fusing Single-Point and Dual-event Dose Response Data For Mycobacterium tuberculosis."

6/23/2014 – Congratulations to the group on the just accepted manuscript at Drug Discovery Today entitled "Minding the Gaps in Tuberculosis Research."





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