Using deep learning, MIT researchers have discovered compounds with high potential to kill drug-resistant bacteria like MRSA. These compounds demonstrate low toxicity against human cells, making them strong drug candidates. MIT’s Antibiotics-AI Project aims to find new antibiotics using deep learning models, and the research has been published in Nature. The project received funding from various organizations.
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MIT Researchers Discover New Class of Antibiotic Candidates Using AI
MIT researchers have used deep learning to identify compounds that can kill drug-resistant bacteria, specifically methicillin-resistant Staphylococcus aureus (MRSA). These compounds showed low toxicity against human cells, making them promising drug candidates.
Key Innovations and Practical Solutions
The researchers were able to analyze the deep-learning model and understand the information it used to make antibiotic potency predictions. This insight could lead to the design of even more effective drugs.
The Antibiotics-AI Project at MIT aims to discover new classes of antibiotics against deadly bacteria over seven years, and this study is part of that mission.
Explainable Predictions and Potent Activity
The deep learning models were trained to identify chemical structures associated with antimicrobial activity, leading to the discovery of potential antibiotic candidates. By combining this information with toxicity predictions against human cells, the researchers identified compounds that could kill microbes with minimal adverse effects on the human body.
The researchers screened about 12 million compounds, leading to the identification of compounds from five different classes that were predicted to be active against MRSA. Further experiments revealed promising antibiotic candidates that were effective in reducing MRSA populations in mouse models.
Future Directions and Collaborative Efforts
The researchers have shared their findings with Phare Bio, a nonprofit involved in the Antibiotics-AI Project, for further analysis and potential clinical use of the compounds. The team is also working on designing additional drug candidates and seeking compounds that can kill other types of bacteria.
The study involved collaborative efforts from various institutions and was funded by several foundations and organizations.
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