Addressing the Metabolic Stability of Antituberculars through Machine Learning

  • Thomas P. Stratton, Alexander L. Perryman, Catherine Vilchèze, Riccardo Russo, Shao-Gang Li, Jimmy S. Patel, Eric Singleton, Sean Ekins, Nancy Connell, William R. Jacobs Jr., Joel S. Freundlich
  • ACS Medicinal Chemistry Letters, September 2017, American Chemical Society (ACS)
  • DOI: 10.1021/acsmedchemlett.7b00299

Applying a machine learning model for metabolic stability

What is it about?

Trying to improve an antitubercular that has metabolic stability problems using a machine learning model. This work combined building a library of virtual analogs and scoring them then making the compounds and testing the metabolic stability.

Why is it important?

This study showed the real difficulty of trying to optimize the whole cell activity and metabolic stability in parallel.

Perspectives

Dr Sean Ekins
Collaborations in Chemistry

This study was extensive in using prospective prediction but it also showed how difficult it is to obtain good ADME properties for some antitubercular compounds and maintain the whole cell activity.

Read Publication

http://dx.doi.org/10.1021/acsmedchemlett.7b00299

The following have contributed to this page: Dr Sean Ekins