Introduction to Novel Antibiotics
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.
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.
Approaches to Designing Molecules
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.
Identifying Promising Compounds
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.
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.
Testing the 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.
Next Steps
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. The researchers are exploring analogs, as well as working on advancing the best candidates preclinically, through medicinal chemistry work. They 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.
Conclusion
The discovery of novel antibiotics using AI is a significant step forward in the fight against drug-resistant bacterial infections. The use of generative AI algorithms to design and screen millions of possible compounds has led to the identification of promising candidates that can combat hard-to-treat infections. Further research and testing are needed to bring these compounds to market, but the potential for AI to accelerate the discovery of new antibiotics is vast.
FAQs
Q: What is the current state of antibiotic resistance?
A: Antibiotic resistance is a growing problem, with nearly 5 million deaths per year attributed to drug-resistant bacterial infections.
Q: How did the researchers use AI to design novel antibiotics?
A: The researchers used generative AI algorithms to design more than 36 million possible compounds and computationally screened them for antimicrobial properties.
Q: What are the next steps for the discovered compounds?
A: Phare Bio is working on further modifying the compounds to make them suitable for additional testing, and the researchers are exploring analogs and advancing the best candidates preclinically.
Q: What is the potential for AI in antibiotic discovery?
A: The potential for AI to accelerate the discovery of new antibiotics is vast, and the use of generative AI algorithms has already led to the identification of promising candidates.
Q: What are the targets for the novel antibiotics?
A: The targets for the novel antibiotics are drug-resistant Neisseria gonorrhoeae and multi-drug-resistant Staphylococcus aureus (MRSA).
