Computers excel at superhuman pattern recognition in images and text; however, their application in biology and medicine is still in its infancy. In this talk, I will discuss our advances over the past decade, which are accelerating discoveries in the crucial and underinvested area of antibiotic discovery. We have pioneered the design of antibiotics using artificial intelligence (AI), achieving proven efficacy in preclinical animal models and demonstrating that machines can effectively create therapeutic molecules. For the first time, we successfully mined the human proteome to identify antibiotic candidates. Building on this success, we hypothesized that similar compounds could be found throughout evolution. We expanded our efforts to extinct species, where our AI-driven approach led to the discovery of the first therapeutic molecules from organisms such as Neanderthals and the woolly mammoth. This work launched the field of molecular de-extinction and yielded preclinical candidates such as neanderthalin, mammuthusin, and elephasin. Furthermore, my lab has broadened our antibiotic discovery initiatives to explore other branches of the tree of life beyond eukaryotes. By computationally analyzing microbial dark matter, we identified nearly one million new antibiotic molecules. These molecules have been made freely available and open access to the scientific community to encourage researchers worldwide to synthesize, characterize, and further develop them. This collaborative effort leveraged machine learning to explore the vast diversity of the microbial world by analyzing 63,410 metagenomes and 87,920 microbial genomes. Additionally, through the computational exploration of thousands of human microbiomes, we and our collaborators discovered a myriad of new antimicrobial agents, including prevotellin-2 produced by the gut microbe Prevotella copri. 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 pandemics.

Professor 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). His research goal is to use the power of machines to accelerate discoveries in biology and medicine. Notably, he pioneered the development of the first computer-designed antibiotic with efficacy in animal models, demonstrating the application of AI for antibiotic discovery and helping launch this emerging field.

His lab is at the forefront of developing computational methods to mine the world’s biological information, leading to the identification of over a million new antimicrobial compounds. These efforts started by exploring the human proteome as a source of antibiotics for the first time. His team was also the first to find therapeutic molecules in extinct organisms, launching the field of molecular de-extinction. Molecular de-extinction has already yielded preclinical antibiotic candidates, such as neanderthalin, mammuthusin, and elephasin. Furthermore, Professor de la Fuente’s lab has broadened its antibiotic discovery initiatives to explore other branches of the tree of life beyond eukaryotes. By computationally analysing microbial dark matter, his team have identified nearly one million new antibiotic molecules. These molecules have been made freely available and open access to the scientific community to encourage researchers worldwide to further develop them. Additional advances from his lab include reprogramming venoms into antimicrobials, developing autonomous nanorobots to treat infections, creating novel resistance-proof antimicrobial materials, and inventing rapid, low-cost diagnostic devices for COVID-19 and other infections.

Professor de la Fuente is an elected Fellow of the American Institute for Medical and Biological Engineering (AIMBE), becoming one of the youngest ever to be inducted. He was selected as the inaugural recipient of the American Institute of Chemical Engineers (AIChE) Langer Prize. Recently, Professor de la Fuente has been awarded the Princess of Girona Prize, the American Society for Microbiology (ASM) Award for Early Career Applied and Biotechnological Research, the ASM Award for Early Career Basic Research, the Rao Makineni Lectureship Award by the American Peptide Society, and was selected as a National Academy of Medicine Emerging Leader in Health and Medicine.