Session: Posters:

The Gram-negative bacterium Neisseria gonorrhoeae is the primary cause of the sexually transmitted infection (STI) gonorrhea. The STI gonorrhea is considered by the World Health Organization to be a significant public health threat because of the increasing number of infections worldwide and the prevalence of drug resistance. However, no new classes of antibacterials effective against N. gonorrhoeae have been developed over the past 50 years. We have explored chemical space via a Bayesian modeling approach, leveraging publicly accessible growth inhibition assay data for N. gonorrhoeae, to predict small growth inhibitors. This presentation will describe the small molecule hits found through this machine learning approach and initial studies to explore their structure-activity relationships and mechanism of action.

Sean Ekins

Appearances