Deep learning model identifies novel antibiotic compounds effective against drug-resistant gonorrhea
By
James J. Collins
The bagel they save for the regulars. Don't skim, savour.
Summary
This article describes a study where researchers developed and validated a deep learning model to discover new antibiotics effective against Neisseria gonorrhoeae, a pathogen that has developed increasing resistance to all recommended treatments. The model explored chemical space for molecules predicted to inhibit N. gonorrhoeae growth, leading to the identification of two selective compounds with novel chemical structures that showed in vitro efficacy against multidrug-resistant strains.
Key quotes
· 3 pulledNeisseria gonorrhoeae, a well-known cause of sexually transmitted disease, has grown increasingly treatment resistant, and identifying effective ways to tackle this pathogen is an utmost priority.
Anahtar et al. developed and validated a deep learning model before using it to explore chemical space for molecules predicted to inhibit N. gonorrhoeae growth.
The authors chose for further testing two selective compounds with chemical structures different from known antibiotics that also showed in vitro efficacy against multidrug-resistant N. gonorrhoeae.
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