A Deep Learning Approach to Antibiotic Discovery (Cell Press)

Feb 24, 2020

The recently published article (Title above) reveals how "Aided by machine learning, scientists find a novel antibiotic able to kill superbugs in mice".

In the following piece by Casey Ross (STAT, https://www.statnews.com/2020/02/20/machine-learning-finds-novel-antibiotic-able-to-kill-superbugs/?utm_campaign=rss), an overview of the enterprise reveals the challenges and the final success. Alongside, it also points out the concerns of the researchers involved in this novel discovery, as the possible use of some overlooked resources "The ability to identify molecules with specific antibiotic properties could aid in the development of drugs to treat so-called orphan conditions that affect a small percentage of the population but are not targeted by drug companies because of the lack of financial rewards", and the expected time to safely commercialise the compound "commercializing halicin would take many months of study to evaluate its toxicity in humans, followed by multiple phases of clinical trials to establish safety and efficacy."

The published article already reveals results obtained from animal experimentation but it does not prepare for the possibility to use Halicin in naturally-occurring infections in a One Medicine framework. Couldn't this be a possibility to join forces for this major problem that both humans and animals face together?


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