Meet the 2025 Fleming Prize winner, Professor César de la Fuente
26 March 2025
Each year, the Microbiology Society awards the Fleming Prize to an individual who has made a distinct contribution to microbiology early in their career.
Ahead of the Fleming Prize Lecture, Early Career microbiologist Kasia Parfitt interviewed Professor César de la Fuente to learn more about his career and how it feels to win a Microbiology Society prize.
How did you feel after you found out you had won the Fleming Prize?
Alexander Fleming was one of my scientific heroes growing up. He discovered penicillin in 1928, ushering in the antibiotic era. It is incredible to think this breakthrough happened less than a century ago. Antibiotics, along with vaccines and clean water, have nearly doubled the average human lifespan. Fleming’s work underpins so much of contemporary medicine and society, and it is an incredible honor to be recognized in the name of one of my scientific heroes whose legacy directly connects to my own research on using machines and AI to discover new antibiotics.
What are your views on the use of AI for research and Microbiology?
Large language models and chatbots are evolving at an astonishing rate. They can be valuable, for instance, by providing coding support for researchers who don’t have a traditional computer science background. In my lab, even those without deep knowledge of microbiology, biology or chemistry can quickly learn from these tools. That said, it is crucial to remain aware of the limitations of these models and to fact-check their outputs. I see AI as a companion or brainstorming partner, rather than an authoritative source. Used responsibly, it can boost scientific progress across many disciplines.
It reminds me of when calculators were first introduced in schools: there was initial resistance, but ultimately calculators freed us to focus on more complex problem-solving. Likewise, we can dedicate more time to making interdisciplinary connections and pushing the boundaries of knowledge, with AI serving as a powerful catalyst.
What attracted you to this area of research?
When I was a teenager, I looked at the biggest problems facing humanity, and antimicrobial resistance (AMR) came out on top. I have been working on AMR ever since, aiming to contribute solutions to this urgent problem. I believe it is the greatest existential threat facing humanity today, and I hope some of the compounds we discover will make it to the clinic and ultimately save lives. My life’s goal has always been to leverage scientific discovery for the betterment of humanity, and tackling AMR is one key way I pursue that mission.
How much do you think the use of your computational biology approaches has sped up antibiotic discovery?
It is estimated that our work has multiplied the speed of antibiotic discovery by a factor of over one million, saving many years of human research and reducing what once took decades of collective work to just hours. This means that people can dedicate their time, energy, and imagination into other avenues, such as coming up with new ideas and hypotheses that might lead to the antibiotic of the future. Of course, the validation and synthesis of the candidates takes a little longer. However, in terms of antibiotic discovery, I think the impact has been huge. It has been thrilling to be part of this progress, and AMR is a prime example of how AI can address urgent scientific challenges in ways that truly benefit society.
How do you know which potential compounds to focus on in your research?
Our research generates millions of compounds, which our AI system then ranks according to their therapeutic potential. From there, a team of microbiologists, chemists, and biochemists evaluates these rankings and selects the most promising compounds for synthesis and characterization. It is a highly interdisciplinary process where human intuition and expertise remain essential, working hand-in-hand with machine intelligence.
What kind of organisms/sources do you focus your antibiotic discovery research on?
We explore virtually everything; essentially all of biology. We think of biology as a vast codebase that can be digitally explored for useful molecules, using the right algorithms. Many people from all around the world have reached out to my lab to inquire about possible antibiotic leads in everything from pig genomes to extremophile databases and human genomic datasets. The range of potential sources is extraordinarily broad, and it is precisely this breadth that makes the field so exciting.
Do you think that there are any competitors out there for your AI model APEX or do you think that it is one of a kind?
There are other antibiotic compound predictors, but I think APEX is probably the best one right now. However, that is not to say it will remain so without continual improvements. We are always expanding our in-house dataset and retraining APEX. Competition is great. If someone builds a better tool that advances science, that is a win for science and for the world.
What challenges did you face in your career, and how did you combat them?
Scientific inquiry is replete with unexpected outcomes. Most things don’t work. Failed experiments are common, and I try to view each so-called “failure” as a stepping stone that points us toward a more promising direction. I encourage my team to pivot or revise approaches whenever something doesn’t pan out, transforming setbacks into opportunities for deeper understanding.
Another big challenge was the initial scepticism around AI being used for AMR. I spent many years struggling to get funding or find a faculty job in traditional microbiology departments. I didn’t even get to interview. I ended up going into other departments, such as bioengineering. That is why it is especially meaningful now to be recognized by the Microbiology Society.
Challenges never really stop if you are pushing the boundaries of knowledge, but working through them is how we grow as scientists. If you apply yourself during those tough periods, you emerge more experienced and resilient.
What is the biggest piece of advice you would give to early career researchers?
The most important factor for me has been my lifelong passion for science. Ever since I was a kid, I have been fascinated by the complexity of biology and the natural world, and that curiosity has carried me through difficult times, like experiments that fail or progress that feels slow. Science has always been both my career and my hobby, and I would gladly pursue it for free. This passion fuels me every day, keeping me motivated as I brainstorm fresh ideas and collaborate with the amazing people who come into my lab from around the world. I am constantly learning from others and feel incredibly fortunate to do so.
I would urge early career researchers to find a field that aligns with their values and excites them. Aim to improve the world in some way, and don’t be afraid to venture into the unknown. That’s where the biggest breakthroughs happen, especially at the intersection of different ideas and disciplines. If you can learn to embrace the uncertainty, that is where innovation really thrives.