As the global health crisis of antibiotic resistance continues to grow, causing more than a million deaths each year, researchers are turning to artificial intelligence to create entirely new weapons against superbugs. According to a recent report by the BBC’s health and science correspondent, James Gallagher, a team at the Massachusetts Institute of Technology (MIT) has used generative AI to invent two potential new antibiotics from scratch. The study, published in the journal Cell, details how these AI-designed compounds have successfully killed drug-resistant gonorrhoea and MRSA in laboratory and animal tests, offering a potential breakthrough in a field that has seen a shortage of new drugs for decades.

How AI is learning to invent new medicines

Unlike previous approaches that used AI to screen thousands of existing chemicals for antibiotic potential, the MIT team has taken a significant step forward by using generative AI to design completely new molecules atom-by-atom. To achieve this, the researchers first trained their AI model by providing it with the chemical structures of known compounds along with data on whether they were effective at slowing the growth of different species of bacteria. This process allowed the AI to learn how different molecular arrangements of atoms like carbon, oxygen, and hydrogen affect bacteria.

Once trained, the AI interrogated a massive dataset of 36 million chemical compounds, including many that either do not yet exist or have not been discovered. The researchers then used two different methods to generate novel antibiotic candidates. The first approach involved identifying a promising chemical fragment from a library and then building upon it, while the second method gave the AI free rein to design a new molecule from the start. The design process also included several important constraints to ensure the outputs were viable. The AI was programmed to weed out any designs that looked too similar to existing antibiotics, to filter out anything predicted to be toxic to humans, and to ensure it was inventing medicines rather than substances like soap.

Promising results and significant hurdles ahead

The AI-driven design process resulted in two new potential drugs specifically targeting the sexually transmitted infection gonorrhoea and methicillin-resistant Staphylococcus aureus, or MRSA, a bacteria that can cause serious infections. When these compounds were manufactured, they were successfully tested on bacteria in the lab and on infected mice. Professor James Collins, one of the MIT researchers, stated that this work shows that generative AI can be used to come up with new molecules cheaply and quickly, potentially starting a “second golden age” in antibiotic discovery.

Despite these promising early results, the path to clinical use is long and filled with challenges. The two compounds are not ready for human trials and will require an estimated one to two more years of refinement before the lengthy process of testing them in people could even begin. A major hurdle is the difficulty of manufacturing the complex molecules designed by the AI. In the case of the gonorrhoea treatments, of the top 80 compounds designed in theory, researchers were only able to successfully synthesize two of them in the lab.

Beyond the scientific and manufacturing challenges, there is also a significant economic problem facing antibiotic development. If a new antibiotic were invented, its use would need to be restricted to preserve its effectiveness against evolving bacteria. This necessary caution makes it difficult for pharmaceutical companies to turn a profit on their investment. Furthermore, experts in the field note that AI drug discovery models still need to be improved. Professor Collins called for “better models” that can more accurately predict how a drug will perform in the human body, not just in a laboratory setting.

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