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.
Featured Image
Why is it important?
This study showed the real difficulty of trying to optimize the whole cell activity and metabolic stability in parallel.
Perspectives
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.
Dr Sean Ekins
Collaborations in Chemistry
Read the Original
This page is a summary of: Addressing the Metabolic Stability of Antituberculars through Machine Learning, ACS Medicinal Chemistry Letters, September 2017, American Chemical Society (ACS),
DOI: 10.1021/acsmedchemlett.7b00299.
You can read the full text:
Contributors
The following have contributed to this page
