Harnessing artificial intelligence for real biological advances—meet César de la Fuente

In an era peppered by breathless discussions about artificial intelligence—pro and con—it makes sense to feel uncertain, or at least want to slow down and get a better grasp of where this is all headed. Trusting machines to do things typically reserved for humans is a little fantastical, historically reserved for science fiction rather than science.

Not so much for César de la Fuente, PhD, the Presidential Assistant Professor in Psychiatry, Microbiology, Chemical and Biomolecular Engineering, and Bioengineering in Penn’s Perelman School of Medicine and School of Engineering and Applied Science. Driven by his transdisciplinary background, de la Fuente leads the Machine Biology Group at Penn: aimed at harnessing machines to drive biological and medical advances.

A newly minted National Academy of Medicine Emerging Leaders in Health and Medicine (ELHM) Scholar, among earning a host of other awards and honors (over 60), de la Fuente can sound almost diplomatic when describing the intersection of humanity, machines and medicine where he has made his way—ensuring multiple functions work together in harmony.

“Biology is complexity, right? You need chemistry, you need mathematics, physics and computer science, and principles and concepts from all these different areas, to try to begin to understand the complexity of biology,” he said. “That's how I became a scientist.”

Making wonder work to understand biology

Since his earliest days, de la Fuente has been fascinated by what he calls the “intricate wonders” of biology. In his late teens, for his undergraduate degree, de la Fuente immersed himself in microbiology, physics, mathematics, statistics, and chemistry, equipping himself with the necessary tools to unravel those biological mysteries.

In his early twenties, determined to understand biology at a fundamental level, de la Fuente decided to pursue a PhD, relocating to Canada from Spain. Overcoming language and cultural barriers, he embraced the challenges and opportunities that lay before him, determined to become a scientist.

His PhD journey centered around programming and digitizing the fundamental workings of biological systems. He specialized in bacteria, the simplest living biological system, as well as proteins and peptides, the least programmable of biomolecules and the “workhorses” of biology that perform every task in life—literally, from moving your mouth while speaking, to blinking your eyes while reading this.

Although his research was successful, the landscape of using machines for biology remained uncharted. Upon completing his PhD, de la Fuente noted that technology (at the time) still did not exist to manipulate peptides in any programmable way. “I felt dissatisfied with the available technologies for programming biology, which relied on slow, painstaking, and unpredictable trial-and-error experimentation. Biology remained elusive in terms of programmability.”

De la Fuente was then recruited by MIT in 2015, at the time a leading home for AI research. However, AI had not yet been applied to biology or molecules. While computers were already adept at recognizing patterns in images and text, de la Fuente saw an opportunity to train computers for applications in biology, connecting the ability for computers to process the massive amounts of data that was becoming increasingly available.

His focus was to incorporate computational thinking into his work, essentially infusing AI into biology—particularly to discover new antibiotics.

“The motivation behind that is antibiotic resistance,” de la Fuente said, adding that bacteria that have developed resistance to known antibiotics kill over one million people per year, projected to grow to 10 million deaths annually by 2050 as resistant strains spread. “Making advances in this hugely disinvested area and coming up with solutions to this sort of critical problem has been a huge motivation for me and for our team.”

Multiple sources for potential new antibiotics—including neanderthals

The typical timeline for discovering antibiotics is three to six years using conventional methods, but de la Fuente’s work in recent years has bucked that trend. With some of the algorithms that his group has developed, what used to take three to six years can now be done in days, or even hours. The potential antibiotic compounds they have identified need more evaluation before they are ready for clinical testing in humans. Even so, the accelerated rate of antibiotic discovery remains a point of pride for de la Fuente’s lab.

This work launched the emerging field of AI for antibiotic discovery, following a pioneering study with his colleagues that led to the design of the first antibiotic using AI. That led de la Fuente to joining Penn as a Presidential Assistant Professor, a post he holds today. Since then, much of his work has focused on pioneering computational and experimental methods to search inside the human body’s own proteins for unknown but potentially useful molecules. By discovering them, his team could learn to manufacture them and use them as templates for antibiotic development.

“In 2021, we performed the first ever exploration of the human proteome—the set of all proteins in the human body—as a source of antibiotics,” he said. “We found them encoded in proteins of the immune system, but also in proteins from the nervous system and the cardiovascular system, digestive system—all throughout our body.”

Just this summer, de la Fuente continued to derive antibiotic discovery from a curious source of inspiration that has been extinct for tens of thousands of years.

What’s next for artificial intelligence in antibiotic discovery?

Recently, de la Fuente’s team applied machine learning to explore the proteomes not just of living humans like us, but of extinct organisms (think: Neanderthals and Denisovans) to find potential new antibiotics, launching the field of what they call “molecular de-extinction" and providing a new framework for thinking about drug discovery. But when asked about what he sees as the future of harnessing machines for human benefit, de la Fuente is remarkably honest when asked about what surprises him about his field.

“I’ve been working in the antibiotics field for a long time, and it has become a sort of under-invested area of research. Sometimes it feels like there’s only a couple of us out there doing this work, so it feels weird sometimes,” he said. With remarkable advances in machine and artificial intelligence in the last half decade, any new support may not be human but machine.

“That combination between machine intelligence and human ingenuity, I think, will be part of the future and we’re going to see a lot of meaningful and important research coming out from that intersection. 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.”