A new class of antibiotics for drug-resistant Staphylococcus aureus (MRSA) bacteria which was discovered using more transparent deep learning models.
The use of artificial intelligence (AI) is proving to be a game-changer when it comes to medicine with the technology now helping scientists to unlock the first new antibiotics in 60 years.
“Our work provides a framework that is time-efficient, resource-efficient, and mechanistically insightful, from a chemical-structure standpoint, in ways that we haven’t had to date”.
These models consist of very large numbers of calculations that mimic neural connections, and no one really knows what’s going on underneath the hood," said Felix Wong, a postdoc at MIT and Harvard and one of the study’s lead authors.
By integrating these toxicity predictions with the previously determined antimicrobial activity, the researchers pinpointed compounds capable of effectively combating microbes with minimal harm to the human body.
The models identified compounds from five different classes, categorised based on specific chemical substructures within the molecules, that exhibited predicted activity against MRSA.
The original article contains 567 words, the summary contains 166 words. Saved 71%. I’m a bot and I’m open source!
This is the best summary I could come up with:
A new class of antibiotics for drug-resistant Staphylococcus aureus (MRSA) bacteria which was discovered using more transparent deep learning models.
The use of artificial intelligence (AI) is proving to be a game-changer when it comes to medicine with the technology now helping scientists to unlock the first new antibiotics in 60 years.
“Our work provides a framework that is time-efficient, resource-efficient, and mechanistically insightful, from a chemical-structure standpoint, in ways that we haven’t had to date”.
These models consist of very large numbers of calculations that mimic neural connections, and no one really knows what’s going on underneath the hood," said Felix Wong, a postdoc at MIT and Harvard and one of the study’s lead authors.
By integrating these toxicity predictions with the previously determined antimicrobial activity, the researchers pinpointed compounds capable of effectively combating microbes with minimal harm to the human body.
The models identified compounds from five different classes, categorised based on specific chemical substructures within the molecules, that exhibited predicted activity against MRSA.
The original article contains 567 words, the summary contains 166 words. Saved 71%. I’m a bot and I’m open source!