Looking to AI to Solve Antibiotic Resistance

by Nathi Magubane

Cesar de la Fuente (left), Fangping Wan (center), and Marcelo der Torossian Torres (right). Fangping holds a 3D model of a unique ATP synthase fragment, identified by their lab’s deep learning model, APEX, as having potent antibiotic properties.

“Make sure you finish your antibiotics course, even if you start feeling better’ is a medical mantra many hear but ignore,” says Cesar de la Fuente of the University of Pennsylvania.

He explains that this phrase is, however, crucial as noncompliance could hamper the efficacy of a key 20th century discovery, antibiotics. “And in recent decades, this has led to the rise of drug-resistant bacteria, a growing global health crisis causing approximately 4.95 million deaths per year and threatens to make even common infections deadly,” he says.

De la Fuente, a Presidential Assistant Professor, and a team of interdisciplinary researchers have been working on biomedical innovations tackling this looming threat. In a new study, published in Nature Biomedical Engineering, they developed an artificial intelligence tool to mine the vast and largely unexplored biological data—more than 10 million molecules of both modern and extinct organisms— to discover new candidates for antibiotics.

“With traditional methods, it takes around six years to develop new preclinical drug candidates to treat infections and the process is incredibly painstaking and expensive,” de la Fuente says. “Our deep learning approach can dramatically reduce that time, driving down costs as we identified thousands of candidates in just a few hours, and many of them have preclinical potential, as tested in our animal models, signaling a new era in antibiotic discovery.” César de la Fuente holds a 3D model of a unique ATP synthase fragment, identified by his lab’s deep learning model, APEX, as having potent antibiotic properties. This molecular structure, resurrected from ancient genetic data, represents a promising lead in the fight against antibiotic-resistant bacteria.

These latest findings build on methods de la Fuente has been working on since his arrival at Penn in 2019. The team asked a fundamental question: Can machines be used to accelerate antibiotic discovery by mining the world’s biological information? He explains that this idea is based on the notion that biology, at its most basic level, is an information source, which could theoretically be explored with AI to find new useful molecules.

Read the full story in Penn Today.

Largest-Ever Antibiotic Discovery Effort Uses AI to Uncover Potential Cures in Microbial Dark Matter

by Eric Horvath

Credit: Georgina Joyce

Almost a century ago, the discovery of antibiotics like penicillin revolutionized medicine by harnessing the natural bacteria-killing abilities of microbes. Today, a new study co-led by researchers at the Perelman School of Medicine at the University of Pennsylvania suggests that natural-product antibiotic discovery is about to accelerate into a new era, powered by artificial intelligence (AI).

The study, published in Cell, the researchers used a form of AI called machine learning to search for antibiotics in a vast dataset containing the recorded genomes of tens of thousands of bacteria and other primitive organisms. This unprecedented effort yielded nearly one million potential antibiotic compounds, with dozens showing promising activity in initial tests against disease-causing bacteria.

“AI in antibiotic discovery is now a reality and has significantly accelerated our ability to discover new candidate drugs. What once took years can now be achieved in hours using computers” said study co-senior author César de la Fuente, PhD, a Presidential Assistant Professor in Psychiatry, Microbiology, Chemistry, Chemical and Biomolecular Engineering, and Bioengineering.

Nature has always been a good place to look for new medicines, especially antibiotics. Bacteria, ubiquitous on our planet, have evolved numerous antibacterial defenses, often in the form of short proteins (“peptides”) that can disrupt bacterial cell membranes and other critical structures. While the discovery of penicillin and other natural-product-derived antibiotics revolutionized medicine, the growing threat of antibiotic resistance has underscored the urgent need for new antimicrobial compounds.

In recent years, de la Fuente and colleagues have pioneered AI-powered searches for antimicrobials. They have identified preclinical candidates in the genomes of contemporary humans, extinct Neanderthals and Denisovans, woolly mammoths, and hundreds of other organisms. One of the lab’s primary goals is to mine the world’s biological information for useful molecules, including antibiotics.

Read the full story in Penn Medicine News.

Artificial Intelligence to Accelerate Antibiotic Discovery

Using AI for discovery of new antibiotics.

The growing threat of antimicrobial resistance demands innovative solutions in drug discovery. Scientists are turning to artificial intelligence (AI) and machine learning (ML) to accelerate the discovery and development of antimicrobial peptides (AMPs). These short strings of amino acids are promising for combating bacterial infections, yet transitioning them into clinical use has been challenging. Leveraging novel AI-driven models, researchers aim to overcome these obstacles, heralding a new era in antimicrobial therapy.

A new article in Nature Reviews Bioengineering illuminates the promises and challenges of using AI for antibiotic discovery. Cesar de la Fuente, Presidential Assistant Professor in Microbiology and Psychiatry in the Perelman School of Medicine, in Bioengineering and Chemical and Biomolecular Engineering in the School of Engineering and Applied Science, and Adjunct Assistant Professor in Chemistry in the School of Arts and Sciences, collaborated with James J. Collins, Termeer Professor of Medical Engineering and Science at MIT, to provide an introduction to this emerging field, outlining both its current limitations and its massive potential.

In the past five years, groundbreaking work in the de la Fuente Lab has dramatically accelerated the discovery of new antibiotics, reducing the timeline from years to mere hours. AI-driven approaches employed in his laboratory have already yielded numerous preclinical candidates, showcasing the transformative potential of AI in antimicrobial research and offering new potential solutions against currently untreatable infections.

Recent advancements in AI and ML are revolutionizing drug discovery by enabling the precise prediction of biomolecular properties and structures. By training ML models on high-quality datasets, researchers can accurately forecast the efficacy, toxicity and other crucial attributes of novel peptides. This predictive power expedites the screening process, identifying promising candidates for further evaluation in a fraction of the time required by conventional methods.

Traditional approaches to AMP development have encountered hurdles such as toxicity and poor stability. AI models help overcome these challenges by designing peptides with enhanced properties, improving stability, efficacy and safety profiles, and fast-tracking the peptides’ clinical application.

While AI-driven drug discovery has made significant strides, challenges remain. The availability of high-quality data is a critical bottleneck, necessitating collaborative efforts to curate comprehensive datasets to train ML models. Furthermore, ensuring the interpretability and transparency of AI-generated results is essential for fostering trust and wider adoption in clinical settings. However, the future is promising, with AI set to revolutionize antimicrobial therapy development and address drug resistance.

Integrating AI and ML into antimicrobial peptide development marks a paradigm shift in drug discovery. By harnessing these cutting-edge technologies, researchers can address longstanding challenges and accelerate the discovery of novel antimicrobial therapies. Continuous innovation in AI-driven approaches is likely to spearhead a new era of precision medicine, augmenting our arsenal against infectious diseases.

Read “Machine learning for antimicrobial peptide identification and design” in Nature Reviews Bioengineering.

The de la Fuente Lab uses use the power of machines to accelerate discoveries in biology and medicine. The lab’s current projects include using AI for antibiotic discovery, molecular de-extinction, reprogramming venom-derived peptides to discover new antibiotics, and developing low-cost diagnostics for bacterial and viral infections. Read more posts featuring de la Fuente’s work in the BE Blog.

Episode 4 of Innovation & Impact: Exploring AI in Engineering

by Melissa Pappas

Susan Davidson, Cesar de la Fuente, Surbhi Goel and Chris Callison-Burch speak on AI in Engineering in episode 4 of the Innovation & Impact podcast.

With AI technologies finding their way into every industry, important questions must be considered by the research community: How can deep learning help identify new drugs? How can large language models disseminate information? Where and how are researchers using AI in their own work? And, how are humans anticipating and defending against potential harmful consequences of this powerful technology?

In this episode of Innovation & Impact, host Susan Davidson, Weiss Professor in Computer and Information Science (CIS), speaks with three Penn Engineering experts about leveraging AI to advance scientific discovery and methods to protect its users. Panelists include:

Chris Callison-Burch, Associate Professor in CIS, who researches the applications of large language models and AI tools in current and future real-world problems with a keen eye towards safety and ethical use of AI;  

Surbhi Goel, Magerman Term Assistant Professor in CIS, who works at the intersection of theoretical computer science and machine learning. Her focus on developing theoretical foundations for modern machine learning paradigms expands the possibilities of deep learning; and

Cesar de la Fuente, Presidential Assistant Professor in Bioengineering, Psychiatry and Microbiology with a secondary appointment in Chemical and Biomolecular Engineering, who leads research on technology in the medical field, using computers to find antibiotics in extinct organisms and identify pre-clinical candidates to advance drug discovery. 

Each episode of Penn Engineering’s Innovation & Impact podcast shares insight from leading experts at Penn and Penn Engineering on science, technology and medicine. 

Subscribe to the Innovation & Impact podcast on Apple MusicSpotify or your favorite listening platforms or find all the episodes on our Penn Engineering YouTube channel.

This story originally appeared in Penn Engineering Today.

New Chip Opens Door to AI Computing at Light Speed

by Ian Scheffler

Computing at the speed of light may reduce the energy cost of training AI. (Narongrit Doungmanee via Getty Images)

Penn Engineers have developed a new chip that uses light waves, rather than electricity, to perform the complex math essential to training AI. The chip has the potential to radically accelerate the processing speed of computers while also reducing their energy consumption.

The silicon-photonic (SiPh) chip’s design is the first to bring together Benjamin Franklin Medal Laureate and H. Nedwill Ramsey Professor Nader Engheta’s pioneering research in manipulating materials at the nanoscale to perform mathematical computations using light — the fastest possible means of communication — with the SiPh platform, which uses silicon, the cheap, abundant element used to mass-produce computer chips.

The interaction of light waves with matter represents one possible avenue for developing computers that supersede the limitations of today’s chips, which are essentially based on the same principles as chips from the earliest days of the computing revolution in the 1960s.

In a paper in Nature Photonics, Engheta’s group, together with that of Firooz Aflatouni, Associate Professor in Electrical and Systems Engineering, describes the development of the new chip. “We decided to join forces,” says Engheta, leveraging the fact that Aflatouni’s research group has pioneered nanoscale silicon devices.

Their goal was to develop a platform for performing what is known as vector-matrix multiplication, a core mathematical operation in the development and function of neural networks, the computer architecture that powers today’s AI tools.

Read the full story in Penn Engineering Today.

Nader Engheta is the H. Nedwill Ramsey Professor in Electrical and Systems Engineering, Bioengineering, Materials Science and Engineering, and in Physics and Astronomy.

Penn Scientists Reflect on One Year of ChatGPT

by Erica Moser

René Vidal, at the podium, introduces the event “ChatGPT turns one: How is generative AI reshaping science?” Bhuvnesh Jain, left at the table, moderated the discussion with Sudeep Bhatia, Konrad Kording, Andrew Zahrt, and Nick Pangakis.

As a neuroscientist surveying the landscape of generative AI—artificial intelligence capable of generating text, images, or other media—Konrad Kording cites two potential directions forward: One is the “weird future” of political use and manipulation, and the other is the “power tool direction,” where people use ChatGPT to get information as they would use a drill to build furniture.

“I’m not sure which of those two directions we’re going but I think a lot of the AI people are working to move us into the power tool direction,” says Kording, a Penn Integrates Knowledge (PIK) University professor with appointments in the Perelman School of Medicine and School of Engineering and Applied Science. Reflecting on how generative AI is shifting the paradigm of science as a discipline, Kording said he thinks “it will push science as a whole into a much more collaborative direction,” though he has concerns about ChatGPT’s blind spots.

Kording joined three University of Pennsylvania researchers from the chemistry, political science, and psychology departments sharing their perspectives in the recent panel “ChatGPT turns one: How is generative AI reshaping science?” PIK Professor René Vidal opened the event, which was hosted by the School of Arts & Sciences’ Data Driven Discovery Initiative (DDDI), and Bhuvnesh Jain, physics and astronomy professor and co-faculty director of DDDI, moderated the discussion.

“Generative AI is moving so rapidly that even if it’s a snapshot, it will be very interesting for all of us to get that snapshot from these wonderful experts,” Jain said. OpenAI launched ChatGPT, a large language model (LLM)-based chatbot, on Nov. 30, 2022, and it rapidly ascended to ubiquity in news reports, faculty discussions, and research papers. Colin Twomey, interim executive director of DDDI, told Penn Today that it’s an open question as to how it will change the landscape of scientific research, and the` idea of the event was to solicit colleagues’ opinions on interesting directions in their fields.

Read the full story in Penn Today.

Konrad Paul Kording is Nathan Francis Mossell University Professor in Bioengineering and Computer and Information Science in Penn Engineering and in Neuroscience in the Perelman School of Medicine.

Herman P. Schwan Distinguished Lecture: “Seeing the Unseen: How AI Redefines Bioengineering” (Dorin Comaniciu, Siemens Healthineers)

Dorin Comaniciu, Ph.D.

We hope you will join us for the 2023 Herman P. Schwan Distinguished Lecture by Dr. Dorin Comaniciu, hosted by the Department of Bioengineering.

Wednesday, December 13, 2023
1:00 PM ET
Location: Wu & Chen Auditorium (Levine 101)
The lecture and Q&A will be followed by a light reception in Levine Lobby.

Speaker: Dorin Comaniciu, Ph.D.
Senior Vice President
Artificial Intelligence and Digital Innovations
Siemens Healthineers

About Dorin Comaniciu:

Dr. Comaniciu serves as Senior Vice President for Artificial Intelligence and Digital Innovation at Siemens Healthineers. His scientific contributions to machine intelligence and computational imaging have translated to multiple clinical products focused on improving the quality of care, specifically in the fields of diagnostic imaging, image-guided therapy, and precision medicine.

Comaniciu is a member of the National Academy of Medicine, the Romanian Academy, and a Top Innovator of Siemens. He is a Fellow of the IEEE, ACM, MICCAI Society, and AIMBE, and a recipient of the IEEE Longuet-Higgins Prize for fundamental contributions to computer vision. Recent recognition of his work includes an honorary doctorate from Friedrich-Alexander University of Erlangen-Nuremberg.

He has co-authored 550 granted patents and 350 peer-reviewed publications that have received 61,000 citations, with an h-index of 102, in the areas of machine intelligence, medical imaging, and precision medicine.

A graduate of University of Pennsylvania’s Wharton School, Comaniciu received a doctorate in electrical and computer engineering from Rutgers University and a doctorate in electronics and telecommunications from Polytechnic University of Bucharest.

He is an advocate for technological innovations that save and enhance lives, addressing critical issues in global health.

About the Schwan Lecture:

The Herman P. Schwan Distinguished Lecture is in honor of one of the founding members of the Department of Bioengineering, who emigrated from Germany after World War II and helped create the field of bioengineering in the US. It recognizes people with a similar transformative impact on the field of bioengineering.

The Immune Health Future, Today

by Christina Hernandez Sherwood

Breaking the code of the immune system could provide a new fundamental way of understanding, treating, and preventing every type of disease. Penn Medicine is investing in key discoveries about immunity and immune system function, and building infrastructure, to make that bold idea a reality.

Several members of the Penn Bioengineering Graduate Group feature in this story which originally featured in the Penn Medicine Magazine.

Image: Courtesy of Penn Medicine Magazine

This grandfather lives with primary progressive multiple sclerosis (MS), an autoimmune disorder that he controls with a medicine that depletes his body of the type of immune cells that make antibodies. So while he has completed his COVID-19 vaccine course, his immune system function isn’t very strong—and the invitation has arrived at a time when COVID-19 is still spreading rapidly. 

You can imagine the scene as an older gentleman lifts a thick, creamy envelope from his mailbox, seeing his own name written in richly scripted lettering. He beams with pride and gratitude at the sight of his granddaughter’s wedding invitation. Yet his next thought is a sober and serious one. Would he be taking his life in his hands by attending the ceremony?

“In the past, all we could do was [measure] the antibody response,” says Amit Bar-Or, the Melissa and Paul Anderson President’s Distinguished Professor in Neurology at the Perelman School of Medicine, and chief of the Multiple Sclerosis division. “If that person didn’t have a good antibody response, which is likely because of the treatment they’re on, we’d shrug our shoulders and say, ‘Maybe you shouldn’t go because we don’t know if you’re protected.’” 

Today, though, Bar-Or can take a deeper dive into his patients’ individual immune systems to give them far more nuanced recommendations. A clinical test for immune cells produced in response to the COVID-19 vaccine or to the SARS-CoV-2 virus itself—not just antibodies—was one of the first applied clinical initiatives of a major new Immune Health® project at Penn Medicine. Doctors were able to order this test and receive actionable answers through the Penn Medicine electronic health record for patients like the grandfather with MS. 

“With a simple test and an algorithm we can have a very different discussion,” Bar-Or says. A test result showing low T cells, for instance, would tell Bar-Or his patient may get a meaningful jolt in immunity from a vaccine booster, while low antibody levels would suggest passive antibody therapy is more helpful. Or, the test might show his body is already well primed to protect him, making it reasonably safe to attend the wedding.

This COVID-19 immunity test is only the beginning. 

Physicians and scientists at Penn Medicine are imagining a future where patients can get a precise picture of their immune systems’ activity to guide treatment decisions. They are working to bring the idea of Immune Health to life as a new area of medicine. In labs, in complex data models, and in the clinic, they are beginning to make sense out of the depth and breadth of the immune system’s millions of as-yet-undeciphered signals to improve health and treat illnesses of all types. 

Penn Medicine registered the trademark for the term “Immune Health” in recognition of the potential impact of this research area and its likelihood to draw non-academic partners as collaborators in its growth. Today, at the south end of Penn’s medical campus, seven stories of research space are being added atop an office building at 3600 Civic Center Blvd., including three floors dedicated to Immune Health, autoimmunity, and immunology research.

The concept behind the whole project, says E. John Wherry, director of Penn Medicine’s Institute for Immunology and Immune Health (I3H), “is to listen to the immune system, to profile the immune system, and use those individual patient immune fingerprints to diagnose and treat diseases as diverse as immune-related diseases, cancer, cardiovascular disease, Alzheimer’s, and many others.”

The challenge is vast. Each person’s immune system is far more complex than antibodies and T cells alone. The immune system is made of multiple interwoven layers of complex defenders—from our skin and mucous membranes to microscopic memory B cells that never forget a childhood infection—meant to fortify our bodies from germs and disease. It is a sophisticated system that learns and adapts over our lifetimes in numerous ways, and it also falters and fails in some ways we understand and others that remain mysterious. And each person’s intricate internal battlefield is in some way unique.

The immune system is not just a set of defensive barricades, either. It’s also a potential source of deep insight about a person’s physiological functioning and responses to medical treatments.

“The immune system is sensing and keeping track of basically all tissues and all cells in our body all the time,” Wherry says. “It is surveying the body trying to clean up any invaders and restore homeostasis by maintaining good health.”

“Our goal is to essentially break the code of the immune system,” says Jonathan Epstein, executive vice dean of the Perelman School of Medicine and chief scientific officer at Penn Medicine. “By doing so, we believe we will be able to determine your state of health and your response to therapies in essentially every human disease.”

Read the full story in Penn Today.

Harnessing Artificial Intelligence for Real Biological Advances—Meet César de la Fuente

by Eric Horvath

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.”

Read the full story in Penn Medicine News.

For a New Generation of Antibiotics, Scientists are Bringing Extinct Molecules Back to Life – and Discovering the Hidden Genetics of Immunity Along the Way

by Devorah Fischler

Marrying artificial intelligence with advanced experimental methods, the Machine Biology Group has mined the ancient past for future medical breakthroughs, bringing extinct molecules back to life. (Image credit: Ella Marushchenko)

“We need to think big in antibiotics research,” says Cesar de la Fuente. “Over one million people die every year from drug-resistant infections, and this is predicted to reach 10 million by 2050. There hasn’t been a truly new class of antibiotics in decades, and there are so few of us tackling this issue that we need to be thinking about more than just new drugs. We need new frameworks.”

De la Fuente is Presidential Assistant Professor in the Department of Bioengineering and the Department of Chemical and Biomolecular Engineering at the University of Pennsylvania School of Engineering and Applied Science. He holds additional primary appointments in Psychiatry and Microbiology in the Perelman School of Medicine.

De la Fuente’s lab, the Machine Biology Group, creates these new frameworks using potent partnerships in engineering and the health sciences, drawing on the “power of machines to accelerate discoveries in biology and medicine.”

Marrying artificial intelligence with advanced experimental methods, the group has mined the ancient past for future medical breakthroughs. In a recent study published in Cell Host and Microbe, the team has launched the field of “molecular de-extinction.”

Our genomes – our genetic material – and the genomes of our ancient ancestors, express proteins with natural antimicrobial properties. “Molecular de-extinction” hypothesizes that these molecules could be prime candidates for safe new drugs. Naturally produced and selected through evolution, these molecules offer promising advantages over molecular discovery using AI alone.

In this paper, the team explored the proteomic expressions of two extinct organisms –Neanderthals and Denisovans, archaic precursors to the human species – and found dozens of small protein sequences with antibiotic qualities. Their lab then worked to synthesize these molecules, bringing these long-since-vanished chemistries back to life.

“The computer gives us a sequence of amino acids,” says de la Fuente. “These are the building blocks of a peptide, a small protein. Then we can make these molecules using a method called ‘solid-phase chemical synthesis.’ We translate the recipe of amino acids into an actual molecule and then build it.”

The team next applied these molecules to pathogens in a dish and in mice to test the veracity and efficacy of their computational predictions.

“The ones that worked, worked quite well,” continues de la Fuente. “In two cases, the peptides were comparable – if not better – than the standard of care. The ones that didn’t work helped us learn what needed to be improved in our AI tools. We think this research opens the door to new ways of thinking about antibiotics and drug discovery, and this first step will allow scientists to explore it with increasing creativity and precision.”

Read the full story in Penn Engineering Today.