AI-Designed Antibiotics Show Promise in Combating Drug-Resistant Superbugs

How Is AI Changing the Fight Against Drug-Resistant Bacteria?

The researchers synthesized a small subset of these AI-designed molecules and found them lethal to superbugs responsible for drug-resistant gonorrhea and stubborn staphylococcus skin infections. "It's a great addition to this emerging field of using AI for antibiotic discovery," says César de la Fuente, a synthetic biologist at the University of Pennsylvania who was not involved in the research. "It shows quite well how generative AI can produce molecules with real-world activity," he adds. "It's elegant and potentially clinically meaningful." A social-enterprise non-profit created by Collins, called Phare Bio, now plans to advance these and other AI-discovered antibiotics toward clinical development. The candidate antibiotics build on earlier finds from Collins' lab -- including halicin, a potent broad-spectrum antibiotic identified in 2020; a more targeted agent called abaucin with activity against Acinetobacter baumannii, a major cause of hospital-acquired infections; and a novel structural class of molecules described last year that proved effective against the superbugs MRSA and VRE. With the team's earlier discoveries, however, Collins and his colleagues were still mining existing chemical libraries, using deep-learning models to spot overlooked compounds with antibacterial potential. The new work sets down a new path altogether: rather than searching for hidden gems in familiar territory, the generative AI platform starts from scratch, conjuring entirely new molecular structures absent from any database. "This is moving from using AI as a discovery tool to using AI as a design tool," Collins says. The shift, he adds, opens new frontiers in antibiotic discovery -- unexplored territory that could harbor the next generation of lifesaving drugs. To train their generative AI model, Collins and his colleagues first used a neural network framework to virtually screen more than 45 million chemical fragments -- the building blocks of would-be drugs -- looking for pieces predicted to have activity against Neisseria gonorrhoeae (the cause of sexually transmitted gonorrhea infections) and Staphylococcus aureus (the germ behind deadly bloodstream infections, pneumonia, and flesh-eating skin disease). Two algorithms then went to work: one assembling the fragments into complete molecular structures, the other predicting which of those structures would pack the strongest antibacterial punch. Together, the algorithms generated more than 10 million candidate molecules, none of which had ever existed before. But then came what MIT study author and computational biologist Aarti Krishnan describes as "a massive bottleneck": very few of these prophesied antibiotics could actually be made in the lab. The researchers manually sifted through the AI hits, filtering for properties suggestive of drug-likeness and synthetic feasibility. They ultimately arrived at a shortlist of around 200 promising designs, 24 of which could be successfully generated. Seven proved to be bona fide antimicrobial agents, as confirmed by laboratory tests, with two showing particularly strong efficacy in mouse models of gonorrhea and staph infections. Notably, each seems to work through a distinct and novel mechanism of action not exploited by existing antibiotics. "That's pretty cool," says Phare co-founder Jonathan Stokes, an antimicrobial chemical biologist at Canada's McMaster University in Hamilton, Ontario. He praises Collins' team for unearthing two highly promising antibiotic leads but notes that the labor-intensive trial-and-error process underscores how far the technology still has to go in producing compounds that can be readily synthesized. "It's a bit of an elephant in the room," he says of synthetic tractability in GenAI drug discovery. "Antibiotics, because of the financial disincentives in this space, have to be cheap," Stokes, who was not involved in the research, says. "They have to be cheap to discover, cheap to develop, and cheap to make. So if there are opportunities to avoid all of these issues with synthetic feasibility, I feel like that is a major advantage." To tackle that challenge, Stokes and his colleagues developed a generative AI tool that designs antibiotic candidates with chemical blueprints tailored for real-world manufacturing, not just computer screens. This tool, called SyntheMol, operates within a more limited chemical space than Collins' GenAI model, choosing only molecules whose building blocks can be synthesized with known, lab-proven reaction steps. That narrows the search parameters to tens of billions of molecules, compared to the 10 possible structures that Collins' model explored. It's enough, however, for SyntheMol to have already yielded several drug candidates that Stokes and his colleagues, through a startup called Stoked Bio, hope to develop into treatments for bacteria linked to Crohn's disease and other hard-to-treat conditions. The team aims to balance the sheer breadth of biochemical possibilities the models can explore with crucial metrics like drug potency, safety, low toxicity, and ease of synthesis. "It's a multi-objective optimization problem," says de la Fuente, who advises Phare and builds his own generative AI models to design antimicrobial peptide drugs. But for now, the tools powering Phare's discovery efforts -- rooted in Collins' approaches -- are already delivering early wins, says Akhila Kosaraju, Phare Bio's CEO and president. "We are getting substantially more potent and less toxic initial compounds," she notes. And backed by the U.S. government's Advanced Research Projects Agency for Health (ARPA-H), along with the philanthropic arm of Google -- which is funding Phare to build open-source infrastructure around AI-guided antibiotic design -- Kosaraju and her colleagues aim to move the most promising candidates into human trials. "We are building what we think is the most novel and robust pipeline of antibiotics in the world," she says.

Using generative AI, researchers design compounds that can kill drug-resistant bacteria

Caption: With help from artificial intelligence, MIT researchers have discovered novel antibiotics that can combat two hard-to-treat infections: a drug-resistant form of gonorrhea and multi-drug-resistant Staphylococcus aureus (MRSA). With help from artificial intelligence, MIT researchers have designed novel antibiotics that can combat two hard-to-treat infections: drug-resistant Neisseria gonorrhoeae and multi-drug-resistant Staphylococcus aureus (MRSA). Using generative AI algorithms, the research team designed more than 36 million possible compounds and computationally screened them for antimicrobial properties. The top candidates they discovered are structurally distinct from any existing antibiotics, and they appear to work by novel mechanisms that disrupt bacterial cell membranes. This approach allowed the researchers to generate and evaluate theoretical compounds that have never been seen before -- a strategy that they now hope to apply to identify and design compounds with activity against other species of bacteria. "We're excited about the new possibilities that this project opens up for antibiotics development. Our work shows the power of AI from a drug design standpoint, and enables us to exploit much larger chemical spaces that were previously inaccessible," says James Collins, the Termeer Professor of Medical Engineering and Science in MIT's Institute for Medical Engineering and Science (IMES) and Department of Biological Engineering. Collins is the senior author of the study, which appears today in Cell. The paper's lead authors are MIT postdoc Aarti Krishnan, former postdoc Melis Anahtar '08, and Jacqueline Valeri PhD '23. Exploring chemical space Over the past 45 years, a few dozen new antibiotics have been approved by the FDA, but most of these are variants of existing antibiotics. At the same time, bacterial resistance to many of these drugs has been growing. Globally, it is estimated that drug-resistant bacterial infections cause nearly 5 million deaths per year. In hopes of finding new antibiotics to fight this growing problem, Collins and others at MIT's Antibiotics-AI Project have harnessed the power of AI to screen huge libraries of existing chemical compounds. This work has yielded several promising drug candidates, including halicin and abaucin. To build on that progress, Collins and his colleagues decided to expand their search into molecules that can't be found in any chemical libraries. By using AI to generate hypothetically possible molecules that don't exist or haven't been discovered, they realized that it should be possible to explore a much greater diversity of potential drug compounds. In their new study, the researchers employed two different approaches: First, they directed generative AI algorithms to design molecules based on a specific chemical fragment that showed antimicrobial activity, and second, they let the algorithms freely generate molecules, without having to include a specific fragment. For the fragment-based approach, the researchers sought to identify molecules that could kill N. gonorrhoeae, a Gram-negative bacterium that causes gonorrhea. They began by assembling a library of about 45 million known chemical fragments, consisting of all possible combinations of 11 atoms of carbon, nitrogen, oxygen, fluorine, chlorine, and sulfur, along with fragments from Enamine's REadily AccessibLe (REAL) space. Then, they screened the library using machine-learning models that Collins' lab has previously trained to predict antibacterial activity against N. gonorrhoeae. This resulted in nearly 4 million fragments. They narrowed down that pool by removing any fragments predicted to be cytotoxic to human cells, displayed chemical liabilities, and were known to be similar to existing antibiotics. This left them with about 1 million candidates. "We wanted to get rid of anything that would look like an existing antibiotic, to help address the antimicrobial resistance crisis in a fundamentally different way. By venturing into underexplored areas of chemical space, our goal was to uncover novel mechanisms of action," Krishnan says. Through several rounds of additional experiments and computational analysis, the researchers identified a fragment they called F1 that appeared to have promising activity against N. gonorrhoeae. They used this fragment as the basis for generating additional compounds, using two different generative AI algorithms. One of those algorithms, known as chemically reasonable mutations (CReM), works by starting with a particular molecule containing F1 and then generating new molecules by adding, replacing, or deleting atoms and chemical groups. The second algorithm, F-VAE (fragment-based variational autoencoder), takes a chemical fragment and builds it into a complete molecule. It does so by learning patterns of how fragments are commonly modified, based on its pretraining on more than 1 million molecules from the ChEMBL database. Those two algorithms generated about 7 million candidates containing F1, which the researchers then computationally screened for activity against N. gonorrhoeae. This screen yielded about 1,000 compounds, and the researchers selected 80 of those to see if they could be produced by chemical synthesis vendors. Only two of these could be synthesized, and one of them, named NG1, was very effective at killing N. gonorrhoeae in a lab dish and in a mouse model of drug-resistant gonorrhea infection. Additional experiments revealed that NG1 interacts with a protein called LptA, a novel drug target involved in the synthesis of the bacterial outer membrane. It appears that the drug works by interfering with membrane synthesis, which is fatal to cells. Unconstrained design In a second round of studies, the researchers explored the potential of using generative AI to freely design molecules, using Gram-positive bacteria, S. aureus as their target. Again, the researchers used CReM and VAE to generate molecules, but this time with no constraints other than the general rules of how atoms can join to form chemically plausible molecules. Together, the models generated more than 29 million compounds. The researchers then applied the same filters that they did to the N. gonorrhoeae candidates, but focusing on S. aureus, eventually narrowing the pool down to about 90 compounds. They were able to synthesize and test 22 of these molecules, and six of them showed strong antibacterial activity against multi-drug-resistant S. aureus grown in a lab dish. They also found that the top candidate, named DN1, was able to clear a methicillin-resistant S. aureus (MRSA) skin infection in a mouse model. These molecules also appear to interfere with bacterial cell membranes, but with broader effects not limited to interaction with one specific protein. Phare Bio, a nonprofit that is also part of the Antibiotics-AI Project, is now working on further modifying NG1 and DN1 to make them suitable for additional testing. "In a collaboration with Phare Bio, we are exploring analogs, as well as working on advancing the best candidates preclinically, through medicinal chemistry work," Collins says. "We are also excited about applying the platforms that Aarti and the team have developed toward other bacterial pathogens of interest, notably Mycobacterium tuberculosis and Pseudomonas aeruginosa." The research was funded, in part, by the U.S. Defense Threat Reduction Agency, the National Institutes of Health, the Audacious Project, Flu Lab, the Sea Grape Foundation, Rosamund Zander and Hansjorg Wyss for the Wyss Foundation, and an anonymous donor.

AI designs new superbug-killing antibiotics for gonorrhoea and MRSA

Researchers have previously used AI to trawl through thousands of known chemicals in an attempt to identify ones with potential to become new antibiotics. Now, the MIT team have gone one step further by using generative AI to design antibiotics in the first place for the sexually transmitted infection gonorrhoea and for potentially-deadly MRSA (methicillin-resistant Staphylococcus aureus). Their study, published in the journal Cell, interrogated 36 million compounds including those that either do not exist or have not yet been discovered. Scientists trained the AI by giving it the chemical structure of known compounds alongside data on whether they slow the growth of different species of bacteria. The AI then learns how bacteria are affected by different molecular structures, built of atoms such as carbon, oxygen, hydrogen and nitrogen. Two approaches were then tried to design new antibiotics with AI. The first identified a promising starting point by searching through a library of millions of chemical fragments, eight to 19 atoms in size, and built from there. The second gave the AI free reign from the start. The design process also weeded out anything that looked too similar to current antibiotics. It also tried to ensure they were inventing medicines rather than soap and to filter out anything predicted to be toxic to humans. Scientists used AI to create antibiotics for gonorrhoea and MRSA, a type of bacteria that lives harmlessly on the skin but can cause a serious infection if it enters the body. Once manufactured, the leading designs were tested on bacteria in the lab and on infected mice, resulting in two new potential drugs.

AI antibiotics for superbugs could lead to 'golden age of discovery'

Artificial intelligence has created two antibiotics that could herald a "golden age" in the fight against superbugs. AI virtually invented more than 50 million compounds and investigated whether they could kill MRSA and gonorrhoea, two of the most common superbugs. More than 52,000 people a year in the UK catch antibiotic-resistant infections, which cause around 2,000 deaths annually. Antimicrobial resistance, which creates superbugs, has been called the "silent pandemic" and the problem of antibiotic resistance is set to get worse. These deadly infections occur when a bacterium is treated with a drug but works out ways of neutralising the medicine. The genetic protections make drugs less effective and can make common infections lethal. Researchers at the Massachusetts Institute of Technology (MIT) used AI to come up with completely new ways of targeting these pathogens in the hope of making a breakthrough against superbugs. "We're excited because we show that generative AI can be used to design completely new antibiotics," Prof James Collins, the leader of the project at MIT, told the BBC. "AI can enable us to come up with molecules, cheaply and quickly and in this way, expand our arsenal, and really give us a leg up in the battle of our wits against the genes of superbugs." AI was used to make as many hypothetical chemical compounds as possible that could target and kill the two bacterial infections.

AI used to design antibiotics that can combat drug-resistant superbugs gonorrhoea and MRSA

Antibiotics are used to kill bacteria, but some infections have become resistant to drugs. It is estimated drug-resistant bacterial infections cause nearly 5 million deaths per year worldwide. New antibiotics that could kill drug-resistant gonorrhoea and MRSA have been developed with the help of artificial intelligence (AI), researchers have said. A team at Massachusetts Institute of Technology (MIT) used generative AI algorithms to design more than 36 million possible compounds. Once computationally screened for antimicrobial properties, the top candidates were shown to be structurally different from any existing antibiotics. They also seemed to work in a new way - by disrupting bacterial cell membranes. Antibiotics kill bacteria, but some infections have become resistant to drugs. It is estimated that drug-resistant bacterial infections cause nearly 5 million deaths per year worldwide. Two compounds were found to be effective against gonorrhoea and MRSA infections - namely NG1 and DN1 respectively. A non-profit organisation is now working on modifying the compounds to make them suitable for further testing. The research appeared on Thursday in scientific journal Cell. MIT Professor James Collins, the paper's senior author, said: "We're excited about the new possibilities that this project opens up for antibiotics development. "Our work shows the power of AI from a drug design standpoint, and enables us to exploit much larger chemical spaces that were previously inaccessible." Read more from Sky News: What do Ukrainians think of Trump-Putin summit? 'Zombie rabbit' pictures explained One of the study's lead authors, MIT postdoc Aarti Krishnan, said: "We wanted to get rid of anything that would look like an existing antibiotic, to help address the antimicrobial resistance crisis in a fundamentally different way. "By venturing into underexplored areas of chemical space, our goal was to uncover novel mechanisms of action."

Artificial Intelligence Designs Two Potential Antibiotics to Combat Drug-Resistant Gonorrhoea and MRSA

Researchers have used AI to design two promising antibiotics that are helpful in combating drug-resistant bacteria, including gonorrhoea and MRSA. AI predicted efficient molecular structures, which were validated in laboratory tests. This innovative method could revolutionize antibiotic discovery, providing faster, cost-efficient development of treatments against superbugs and addressing one of the most urgent public health threats. In a groundbreaking development, researchers at the Massachusetts Institute of Technology (MIT) have harnessed artificial intelligence (AI) to design two new innovative antibiotics capable of fighting drug-resistant superbugs, including gonorrhoea and MRSA. The method makes a significant step forward in addressing the growing global threat caused by antimicrobial resistance (AMR), which has been explained by the World Health Organization as one of the most urgent public health challenges of the 21st century. One of the newly developed antibiotics has shown remarkable progress against MRSA (methicillin-resistant Staphylococcus aureus), a dangerous pathogen responsible for severe hospital-acquired infections. MRSA is notorious for evading standard antibiotics, leading to prolonged illnesses and higher mortality rates. The second compound concentrates on drug-resistant strains of Neisseria gonorrhoeae, the bacterium responsible for gonorrhoea. Rising cases of antibiotic-resistant gonorrhoea have raised serious concerns about treatment failures and broader public health risks. Laboratory tests confirmed that both compounds successfully killed the targeted bacteria, even those resistant to multiple existing medications. While these results are preliminary, they provide a strong base for further preclinical and clinical testing. The successful use of AI in developing these antibiotics signals a new era in drug discovery. By reducing the time and cost involved with developing new drugs, AI could enhance the response to emerging superbugs and other infectious diseases. This is particularly important as the antibiotic pipeline has slowed in recent years, making healthcare systems vulnerable to resistant infections. Experts say that AI-designed antibiotics are not a complete solution but rather an important tool in the ongoing battle against AMR. Complementary strategies, including proper antibiotic stewardship, infection prevention, and global surveillance, remain crucial to handling the spread of resistant bacteria. Researchers aim to advance the new compounds into further preclinical trials to evaluate their safety, efficacy, and side effects in humans. If successful, these antibiotics could provide new treatment for patients affected by resistant infections and save countless lives globally. Q1. What are superbugs? A1. Superbugs are bacteria resistant to multiple antibiotics, making infections harder to treat. They pose a serious global health risk. Q2. How did AI help in developing these antibiotics? A2. AI analyzed vast chemical data to predict molecular structures likely to combat resistant bacteria effectively. (You can now subscribe to our Economic Times WhatsApp channel)