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On December 9, Francesca Grisoni and Antoni Forner Cuenca will host Professor C茅sar de la Fuente of the University of Pennsylvania (USA).

Title: AI for Antibiotic Discovery

Abstract:

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.

Bio:

C茅sar 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鈥檚 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, de la Fuente鈥檚 lab has broadened its 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, these efforts have dramatically accelerated antibiotic discovery, reducing the time required to identify preclinical candidates from years to just a few hours.

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.

Prof. de la Fuente is an NIH MIRA investigator and has received recognition and research funding from numerous organizations. De la Fuente has received over 80 national and international awards. He 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 recognized by MIT Technology Review as one of the world鈥檚 top innovators for 鈥渄igitizing evolution to make better antibiotics.鈥� He was selected as the inaugural recipient of the Langer Prize and as an ACS Kavli Emerging Leader in Chemistry, an ASM Distinguished Lecturer, Waksman Foundation Lecturer, and received the Mikl贸s Bondanszky Award, AIChE鈥檚 35 Under 35 Award, Society of Hispanic Professional Engineers Young Investigator Award, and the ACS Infectious Diseases Young Investigator Award. He also received the Thermo Fisher Award, as well as the EMBS Academic Early Career Achievement Award 鈥淔or the pioneering development of novel antibiotics designed using principles from computation, engineering, and biology.鈥� Recently, Prof. de la Fuente has been awarded the prestigious Princess of Girona Prize, the 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. De la Fuente serves on the editorial boards of numerous scholarly journals and is currently an Associate Editor of Drug Resistance Updates (IF= 24.3; the premier international drug resistance journal), Nature Communications Biology, Bioactive Materials (IF = 18.9), Bioengineering & Translational Medicine, and Digital Discovery.

He has been named a Highly Cited Researcher by Clarivate multiple times. Prof. de la Fuente has given over 300 invited lectures, including numerous Keynote and Named Lectures, and has also spoken at TEDx. He has co-authored an influential book on machine learning for drug discovery, secured multiple patents, and published over 160 peer-reviewed papers in top-tier journals such as Cell, Science, Cell Host Microbe, Nature Biomedical Engineering, Nature Communications, PNAS, ACS Nano, Nature Chemical Biology, and Advanced Materials.