César de la Fuente, Ph.D.

Presidential Assistant Professor at UPenn

César de la Fuente, Ph.D. is a Presidential Assistant Professor at the University of Pennsylvania, where he is leading the Machine Biology Group to integrate synthetic biology, microbiology, and AI. Prof. de la Fuente seeks to expand nature’s repertoire to build novel synthetic molecular tools and devise therapies that nature has not previously discovered. The Machine Biology Group aims to develop computer-made tools and medicines that will replenish our current antibiotic arsenal, engineer the microbiome and provide novel approaches to study and control brain function and behavior. De la Fuente is an NIH MIRA investigator, a BBRF Young Investigator, and has received recognition and research funding from numerous other groups.

Prof. de la Fuente was recognized by MIT Technology Review in 2019 as one of the world’s top innovators for “digitizing evolution to make better antibiotics”. He was selected as the inaugural recipient of the Langer Prize (2019), an ACS Kavli Emerging Leader in Chemistry (2020), AIChE’s 35 Under 35 Award (2020), and received the ACS Infectious Diseases Young Investigator Award (2020). In addition, he was named a Boston Latino 30 Under 30, a 2018 Wunderkind by STAT News, a Top 10 Under 40 of 2019 by GEN, a Top 10 MIT Technology Review Innovator Under 35 (Spain), 30 Rising Leaders in the Life Sciences by In Vivo magazine, and he received the 2019 Society of Hispanic Professional Engineers Young Investigator Award. His scientific discoveries have yielded over 75 peer-reviewed publications and multiple patents.

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Title: Antibiotic Discovery by Means of Computers

Until now, the natural world has supplied us with antibiotics. Bacteria, however, are increasingly resistant to these drugs. The next generation of antibiotics will likely come not from nature but from computer-based discovery. Computer-driven approaches have the potential to outperform humans, as demonstrated for pattern recognition of images and text. In order for machines to discover novel drugs and optimize antimicrobial properties, they have to be able to understand, read and write molecules. In this talk, Prof. de la Fuente will describe their efforts in developing computational approaches for antibiotic discovery. He will discuss how he has trained a computer to execute a fitness function following Darwin’s algorithm of evolution to select for structures that interact with bacterial membranes, yielding the first artificial antimicrobials that kill bacteria both in vitro and in animal models. Prof. de la Fuente lab has also developed pattern recognition algorithms to mine the human proteome, identifying throughout the body thousands of antibiotics encoded in proteins with unrelated biological function. Computer-made drugs may help to replenish our arsenal of effective drugs.

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On the Web

https://delafuentelab.seas.upenn.edu/

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