AI-Powered 'Virtual Cell' Models: The Next Frontier in Biomedical Research and Personalized Medicine

Priscilla Chan Sees AI Models of Cells as the Next Leap in Biology and Medicine

Expertise Energy, Solar Power, Renewable Energy, Climate Issues, Virtual Power Plants, Grid Infrastructure, Electric Vehicles, Plug-in Hybrids, Energy-Savings Tips, Smart Thermostats, Portable Power Stations, Home Battery Solutions, EV Charging Infrastructure, Home Finding the right medicine to treat conditions like anxiety and depression can be tricky. A doctor will start you on one medication that's typically well-tolerated and effective, but it could do nothing for you -- or have terrible side effects. Sometimes it takes months of trial and error to find something that works. It's an incredibly common issue. Dr. Priscilla Chan told an audience at South by Southwest Wednesday that it could be streamlined if doctors could check drugs against a generative AI model of your cells and systems. Chan, who co-founded the Chan Zuckerberg Initiative with her husband, Meta founder and CEO Mark Zuckerberg, said using AI could be the next big leap for biomedical research. "The hope is with those models we'll be able to answer some of the hardest questions in biology," Chan said. Artificial intelligence has been a hot topic for just about everyone since its breakout moment with the debut of the ChatGPT AI chatbot in late 2022. This week, it was a major focus at SXSW in Austin, Texas, with conversations around trust, accountability and the future of work. Last year, two scientists in Google's DeepMind AI unit won the Nobel Prize in chemistry for their work using AI to predict the structure of proteins. As for how this technology can advance science and medicine, it could take years, if not decades. And these AI models will likely just speed up actual lab research, not replace it. But Chan sees a world of possibilities. Chan, a pediatrician, said much of how the human body works still eludes the understanding of science. Sure, it has been a couple of decades since researchers cracked the human genome, but genetics offers just a roadmap. Chan used the analogy of a Lego kit of the Millennium Falcon from Star Wars -- the genetic code is the instruction packet. However, we still don't know how the individual pieces come together to form the spaceship. And when one part doesn't seem to fit right, that's where medicine needs to step in. Beyond the gaps in scientific knowledge about biology, we also have a limited understanding of how biology works within individual people. Based on a small number of samples, we have extrapolations about how the body is supposed to work, but that is a tiny dataset that doesn't come close to representing the sheer diversity of humanity. An AI model could help describe what is happening in one individual's cells -- personalizing medicine so that your treatment differs from mine. "If we build the right data and AI models, we can better understand specifically what is making us healthy and what is making us sick," Chan said. Current research techniques are also slow and expensive in developing new drugs and treatments. Ideas have to be tested in a physical laboratory setting, which takes a tremendous amount of time and resources. Chan doesn't suggest eliminating the existing physical "wet laboratory" research. But a machine learning model -- a hallmark of AI -- can help identify drug candidates with a higher probability of working, meaning it might take fewer real-world tests to reach a workable solution. The models won't always be correct. They'll offer solutions and ideas that don't work out, maybe physically impossible ideas, but that's why there needs to be a filter of real human scientists tackling the ideas a model produces. "It's not going to give us the full answer," Chan said. "I don't want you to think that scientists are just going to talk to a model and get all of the answers they need." The machines can help scientists find better questions, Chan said. "It's going to be the hypothesis generator," she said. While many companies and researchers are looking at ways to use AI in hospitals and the treatment of patients, Chan's focus is on advancing the basic biological research that makes future advances possible. She sees AI as a potential major leap for science, akin to the invention of the microscope, the X-ray, the MRI or the sequencing of the human genome. "Health and medicine, it moves in leaps," she said. "There are decades when research gets stuck, and then someone invents a new technology that completely changes how we see the human body."

CZI's Priscilla Chan on 'Virtual Cells' AI Models to Cure Diseases | PYMNTS.com

The head of the Chan Zuckerberg Initiative (CZI) believes that artificial intelligence (AI) can accelerate medical research such that it would be possible within 10 to 20 years to stop a disease very early on in its tracks. AI can "put us in a world where we aren't just trying to treat disease when it's out of control. We're actually preventing it at the earliest stages," said Priscilla Chan, who leads the philanthropic organization founded by her husband, Meta CEO Mark Zuckerberg, and herself. She detailed that vision during a presentation Thursday (March 13) at SXSW 2025. The mechanism for this future is a "virtual cell" model that could revolutionize how scientists understand human biology and develop treatments for all diseases. It is an AI model that harnesses generative AI not trained on text but on biological data. "What if you showed an AI model three images of the human heart at an atomic level? What if you showed it videos of individual cells interacting with other cells? What if you taught it the molecular code and all the structures inside every one of your cells? What you would get is a powerful simulation of how the human cell works," she said during a firechat chat at SXSW 2025 in Austin, Texas. "We call that the virtual cell model. And we think a virtual cell model would completely change the way we understand health and medicine," she said. CZI is the recipient of Chan and Zuckerberg's pledge to give away 99% of their Facebook shares over their lifetimes. It focuses on science, education and community building. In February, the initiative launched the Billion Cells Project, aiming to generate an unprecedented one billion cell dataset. This project expands upon their previous work and plans to use a large-scale cluster of more than 1,000 GPUs to train AI models for advancing biological research. Read more: Google's AI 'Co-Scientist' Helps Unearth Research Ideas Chan said the virtual cell model could significantly speed up the pace of medical discoveries by enabling researchers to test hypotheses computationally before moving to laboratory experiments. This could transform drug development, potentially reducing the process from years to days and making treatments for rare diseases more affordable. "Right now, the drug discovery process is incredibly difficult. It usually takes decades of trial and error and billions and billions of dollars of investment," Chan said. "A virtual cell changes that equation. Instead of testing candidate molecules one by one in the lab, you can model the disease in software. You can test a million potential therapies." Chan said scientists currently don't know a lot about how cells work. "What does each gene actually do, and what happens when there's errors in them? And how do those trillions of cell types come from a single fertilized egg to make each one of your unique cells?" she said. "Those are the deepest, oldest questions in human biology, and if a virtual cell can help us answer those questions, we will be so much closer to actually cure and preventing heart disease, neurodegeneration, cancer, maybe all disease," Chan added. She sketched out three scenarios of how life would change if every scientist and doctor had access to the virtual cell model. Currently, medical researchers study a small number of samples and extrapolate the treatment to the general population. Using the virtual cell, physicians can predict what diseases each patient is susceptible to and how individuals respond to different treatments. The drug discovery process typically takes decades of trial and error and billions of dollars. Successful drugs can be derailed by side effects, and treatments for rare diseases are not viable economically because the pool is too small. A virtual cell can test a million potential therapies and screen out drugs that don't work -- leaving promising candidates. The concept behind a large language model can help develop a virtual cell that can detect if there are signs of tumors. Today, by the time most people develop symptoms, the disease has taken hold and the damage is can be irreversible.