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Machines have the potential to outperform humans and revolutionize our world. In this talk, I will describe our efforts to use machines to develop computational approaches for antibiotic discovery, as well as low-cost rapid diagnostics.

Computers can already be programmed for superhuman pattern recognition of images and text. In order for machines to discover novel antibiotics, they have to first be trained to sort through the many characteristics of molecules and determine which properties should be retained, suppressed, or enhanced to optimize antimicrobial activity. Said differently, machines need to be able to understand, read, write, and eventually create new molecules. I will discuss how we trained a computer to execute a fitness function following a Darwinian algorithm of evolution to select for molecular structures that interact with bacterial membranes, yielding the first artificial antimicrobials that kill bacteria both in vitro and in relevant animal models.

My 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, and has applied computational tools to successfully reprogram venoms into novel antimicrobials. I will also describe the development of diagnostic biosensors for COVID-19, further substantiating the exciting potential of machine biology.

Computer-generated designs and innovations at the intersection between machines and biology may help to replenish our arsenal of effective drugs and generate novel diagnostics, providing much needed solutions to global health problems caused by infectious diseases.


César de la Fuente

César de la Fuente is a Presidential Assistant Professor at the University of Pennsylvania, where he leads the Machine Biology Group whose goal is to combine the power of machines and biology to understand, prevent, and treat infectious diseases. Current application areas in his lab include developing novel approaches for antibiotic discovery, building tools for microbiome engineering, and creating low-cost diagnostics. Specifically, he pioneered the development of the world’s first antibiotic designed by a computer with efficacy in animal models (Nature Communications 2018), designed pattern recognition algorithms for antibiotic discovery, successfully reprogrammed venoms into novel antimicrobials (PNAS 2020, Nature Comm Biol 2018), created novel resistance-proof antimicrobial materials (ACS Nano 2021), and invented rapid low-cost diagnostics for COVID-19 and other infectious diseases.

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), and received the Nemirovsky Prize (2020), AIChE’s 35 Under 35 Award (2020), and 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 and received the 2019 Society of Hispanic Professional Engineers Young Investigator Award in addition to the Young Innovator in Cellular and Molecular Bioengineering and the Biomedical Engineering Society (BMES) CMBE Rising Star Award, both in 2021.

His scientific discoveries have yielded over 85 peer-reviewed publications, including papers in Nature Communications, PNAS, ACS Nano, Cell, Nature Communications Biology, and multiple patents.



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