AI helped scientists find a new antibiotic: such discoveries have not been made for 60 years

Artificial intelligence (AI) has fueled medical breakthroughs. This technology has helped scientists discover the first new antibiotics in 60 years that can fight drug-resistant, deadly bacteria, reports EuroNews.

Photo: IStock

A new class of antibiotics for drug-resistant Staphylococcus aureus (MRSA) bacteria is discovered using AI models.

A new compound could combat a drug-resistant bacterium that kills thousands of people around the world every year. This could prove to be a turning point in the fight against antibiotic resistance.

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“We were able to see what the models learned to make predictions that certain molecules might make good antibiotics,” says James Collins, a professor of medical engineering and science at the Massachusetts Institute of Technology (MIT) and one of the study’s authors.

The results were published in the journal Nature and co-authored by a team of 21 researchers.

"The black box was opened"

The project team used a deep learning model to predict the activity and toxicity of the new compound.

Deep learning involves using artificial neural networks to automatically learn and represent data without explicit programming.

It is increasingly used in drug development, predicting drug properties and optimizing the development process. In this case, the researchers focused on methicillin-resistant Staphylococcus aureus (MRSA).

Infections caused by MRSA can range from mild skin infections to more severe and potentially life-threatening conditions such as pneumonia and bloodstream infections.

According to the European Center for Disease Prevention and Control (ECDC), there are almost 150 MRSA infections each year in the European Union, while almost 000 people die annually from such antimicrobial-resistant infections.

A team of MIT researchers has trained a significantly expanded AI model using large datasets.

To generate the training data, approximately 39 compounds were assessed for their antibiotic activity against MRSA. Subsequently, both the obtained data and details of the chemical structure of the compounds were entered into the model.

“These models consist of a very large number of calculations that simulate neural connections. Nobody really knows what's going on under the hood of the model—nobody knows what's in the black box,” said Felix Wong, PhD, one of the study's lead authors.

To refine the selection of potential drugs, the researchers used three additional deep learning models. These models were trained to evaluate the toxicity of compounds on three different types of human cells.

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By combining these toxicity predictions with previously established antimicrobial activity, the researchers identified compounds that can effectively fight microbes with minimal harm to the human body.

Approximately 12 million commercially available compounds were screened using this set of models.

The models identified compounds from five different classes, selected based on specific chemical substructures within the molecules, that exhibited predicted activity against MRSA.

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