The researchers also used three additional deep-learning models to assess the toxicity of compounds on three types of human cells. By integrating these toxicity predictions with the previously determined antimicrobial activity, they pinpointed compounds capable of effectively combating microbes with minimal harm to the human body. From approximately 12 million commercially available compounds, the models identified five different classes of compounds with predicted activity against MRSA. Two promising antibiotic candidates were identified from the same class, which reduced the MRSA population by a factor of 10 in mouse models.
Key takeaways:
- A new class of antibiotics for drug-resistant Staphylococcus aureus (MRSA) bacteria has been discovered using deep learning models.
- The use of artificial intelligence (AI) is proving to be a game-changer in medicine, helping scientists to unlock the first new antibiotics in 60 years.
- The team behind the project used a deep-learning model to predict the activity and toxicity of the new compound, focusing on methicillin-resistant Staphylococcus aureus (MRSA).
- By integrating toxicity predictions with the previously determined antimicrobial activity, the researchers pinpointed compounds capable of effectively combating microbes with minimal harm to the human body.