How we searched for antibiotics beyond familiar chemical space
In this post, I explain how we used generative models to search for antibiotic candidates beyond the familiar chemical space of existing libraries.
The problem
Antibiotic discovery has slowed down while resistant bacteria have continued to spread. One reason is that our current molecular libraries are biased toward chemistry we already know well. That matters even more for Gram-negative bacteria such as E. coli, because their outer membrane blocks many molecules before those molecules can do any useful work.
The idea
In this paper, we asked whether generative AI could help us search beyond that familiar chemical space. We did not want to stop at drawing new molecules on a screen. We wanted to build a workflow that could generate unfamiliar candidates, narrow them down intelligently, and then test them in the lab.
How it works
We trained a chemical language model on drug-like molecules and natural products, then refined it with a diverse set of antibiotic scaffolds. We filtered the resulting candidates with predictive modeling and expert curation so that we could prioritize molecules that looked both synthetically accessible and promising for antibiotic activity. We then synthesized and tested the shortlisted compounds, and we followed the best lead with an iterative round of optimization through 40 derivatives.
What the paper showed
That process produced a clear lead. We identified a candidate with potent activity against methicillin-resistant Staphylococcus aureus, and our follow-up chemistry generated a broader set of active compounds. In the paper, we report that 30 derivatives showed activity against S. aureus and 17 against Escherichia coli. One lead compound, D8, reached submicromolar potency against S. aureus and single-digit micromolar potency against E. coli. Our mechanistic experiments pointed to reductive generation of reactive species as the main mode of action.
Why it matters
What makes this project interesting to me is that we moved generative chemistry out of the purely virtual realm. We did not stop at “the model made some molecules.” We followed the harder path from generated structures to synthesis, biological testing, and lead refinement. That makes the argument more convincing: AI can help us explore new parts of chemical space rather than simply rearrange the parts we already search.
Limits
We are still in early-stage antibiotic discovery, not at the stage of a finished medicine. Much of the work lies in turning a huge virtual set into a tractable experimental program, and the outcome is a lead series, not an endpoint. But that is exactly why the paper matters: it shows what it takes to turn generative suggestions into experimental traction.
Read the paper
Kollen, M. F.; Schuh, M. G.; Kretschmer, R.; Hesse, J.; Schum, D.; Chen, J.; Bohne, A. I.; Halter, D. P.; Sieber, S. A. JACS Au 2025, 5(9), 4249-4259. 10.1021/jacsau.5c00602