Cesar de la Fuente
Presidential Associate Professor, University of Pennsylvania
Accelerating antibiotic discovery using AI
Abstract
For a century, antibiotic discovery has depended on labor-intensive “dirt mining”: scientists collected soil or water samples and painstakingly screening them for active compounds. That trial-and-error paradigm is too slow to keep pace with escalating antimicrobial resistance. Over the past decade, our laboratory has replaced it with digital discovery, using artificial-intelligence (AI) models to mine biology.
We have pioneered the design of antibiotics using AI, producing molecules that show efficacy in preclinical animal models. By systematically mining the human proteome, we uncovered thousands of novel antimicrobials that appear to form a distinct branch of peptide-based immunity, shaping host defense and broader physiological functions. Subsequently, we hypothesized that similar compounds could be found throughout evolution. By expanding our efforts to ancient biology, we discovered therapeutic molecules from organisms such as Neanderthals and the woolly mammoth, launching the field of molecular de-extinction and yielding preclinical candidates such as neanderthalin and mammuthusin.
Determined to explore the full tree of life, we then turned to its other two major domains, Bacteria and Archaea. By computationally analyzing the global microbiome, we identified nearly one million new antibiotic molecules, all of which have been made freely available and open access to encourage worldwide collaboration. Additionally, by examining thousands of human microbiomes, we and our collaborators discovered a myriad of new antimicrobial agents, including prevotellin-2 from the gut microbe Prevotella copri. Most recently, we have also digitally mined archaea, an underexplored domain of life, identifying a new class of antibiotics called archaeasins.
Collectively, our efforts have dramatically accelerated antibiotic discovery, reducing the time required to identify preclinical candidates from years to just a few hours. I believe we are on the cusp of a new era in science where advances enabled by AI will help control antibiotic resistance, infectious disease outbreaks, and future pandemics.
Bio
César de la Fuente is a Presidential Associate Professor at the University of Pennsylvania, where he leads the Machine Biology Group. He completed postdoctoral research at the Massachusetts Institute of Technology (MIT) and earned a PhD from the University of British Columbia (UBC). He is best known for pioneering computational and artificial intelligence approaches to antibiotic discovery, which have drastically accelerated the time needed to identify preclinical candidates, from years to hours. These candidates show promise for therapeutic intervention against currently untreatable infections. Moreover, he spearheaded the discovery of therapeutic molecules from extinct organisms, and his lab has uncovered a myriad of novel peptide molecules across the tree of life, revealing a previously unrecognized branch of host immunity. Professor de la Fuente has received numerous awards for his contributions, including the Princess of Girona Prize and the Fleming Prize. He is a Fellow of the American Institute for Medical and Biological Engineering, a Sloan Fellow, and a National Academy of Medicine Emerging Leader. De la Fuente has authored more than 170 publications and holds multiple patents.