Researchers at MIT leverage artificial intelligence to create novel antibiotics aimed at resistant strains of gonorrhoea and MRSA.

Researchers at MIT leverage artificial intelligence to create novel antibiotics aimed at resistant strains of gonorrhoea and MRSA.

Artificial intelligence (AI) is making significant strides in the development of new drugs to combat drug-resistant bacteria, such as Neisseria gonorrhoeae (causing gonorrhoea) and Staphylococcus aureus (including MRSA).

A team of researchers from the Massachusetts Institute of Technology (MIT) has been at the forefront of this innovation, using generative AI algorithms to explore vast chemical spaces beyond existing drugs. Their aim was to uncover novel mechanisms of action by venturing into underexplored areas of chemical space, getting rid of anything that resembles existing antibiotics to help address the antimicrobial resistance (AMR) crisis in a fundamentally different way.

The researchers generated over 36 million potential compounds and selected the best candidates to kill the bacteria. One of these AI-generated compounds, named NG1, was highly effective at killing gonorrhoea bacteria in a lab dish and a mouse model. Similarly, six molecules generated for MDRSA were effective against bacteria in a lab dish.

These AI-designed molecules are structurally distinct from known antibiotics and utilize novel mechanisms, such as disrupting bacterial cell membranes, making them effective against bacteria resistant to current treatments. This is a significant development in the ongoing battle against superbugs, as bacteria evolve to make existing treatments ineffective.

The findings of the study, published in the journal Cell, could help create and evaluate potential new compounds to target other species of bacteria. If the compounds continue to show promise, they could eventually be tested in clinical trials.

AI also assists in discovering antibiotic candidates from unconventional sources, such as ancient microbes (Archaea), by analyzing their proteins and DNA to find novel antimicrobial compounds that might evade current resistance mechanisms.

The work of the scientists shows the power of AI from a drug design standpoint, enabling them to explore larger chemical spaces previously inaccessible. It accelerates drug discovery by predicting molecular structures efficiently, generating and screening millions of hypothetical compounds computationally for antimicrobial activity, and validating AI-generated candidates in vitro (lab tests) and in vivo (animal models).

Collaborating with Phare Bio, a nonprofit biotech company, the researchers continue to test these compounds in the lab, bringing us one step closer to addressing the growing issue of antibiotic-resistant superbugs, which could kill 39 million people by 2050, according to a related study.

James Collins, an MIT professor and one of the study's authors, stated that the work opens up new possibilities for antibiotics development. Aarti Krishnan, another MIT researcher and one of the study's authors, emphasized that the goal was to uncover novel mechanisms of action by venturing into underexplored areas of chemical space.

In 2021, drug-resistant bacterial infections contributed to an estimated 4.71 million deaths globally, and this figure is expected to rise in the coming decades. The development of AI-designed drugs offers a beacon of hope in this urgent global health crisis.

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