Tag: drug discovery
Meet the newest Forbes 30 under 30
Gabriele Corso was a computer science PhD student at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL), where his research focused on the intersection of machine learning and molecular biology. His cofounder Saro Passaro was also a research scientist at MIT and previously worked at Meta. The two have trained AI models for predicting biomolecular structures and how molecules interact within the body, which could eventually help with drug discovery. Used by thousands of global organizations and downloaded more than 1M times, these open source models are the basis for Boltz, a company Corso and Passaro cofounded with Jeremy Wohlwend to improve therapeutic design using AI. Learn more
What will be the first AI-designed drug? These disease-fighting antibodies are top contenders
Antibodies — immune proteins that recognize foreign molecules, such as those made by pathogens, with exquisite specificity — have been a challenge for AI to design. AI models such as AlphaFold have struggled to predict the shape of flexible loop regions of antibodies, which they use to recognize their targets.But new tools developed in the past year — including an updated version of AlphaFold — have proved better at modelling these flexible regions, says Gabriele Corso, a machine-learning scientist at the Massachusetts Institute of Technology in Cambridge. Progress in antibody design has followed.
In October, Corso and his colleagues described the BoltzGen model in a preprint, showing that it can adroitly design ‘nanobodies’ — small, simple antibodies resembling molecules made by sharks and camels — against proteins implicated in cancer, viral and bacterial infections and other diseases. In most cases, the researchers identified antibodies with strong target binding after expressing just 15 of the most-promising designs in cells and testing them in laboratory experiments. However, the molecules were not tested in disease models. Learn more
MIT scientists debut a generative AI model that could create molecules addressing hard-to-treat diseases
More than 300 people across academia and industry spilled into an auditorium to attend a BoltzGen seminar on Thursday, Oct. 30, hosted by the Abdul Latif Jameel Clinic for Machine Learning in Health (MIT Jameel Clinic). Headlining the event was MIT PhD student and BoltzGen’s first author Hannes Stärk, who had announced BoltzGen just a few days prior. Learn more
2025’s Fiercest Women in Life Sciences
The life sciences mission of Najat Khan, Ph.D., was forged in hospital hallways.Her parents, a trauma surgeon and a gynecologist-turned-radiologist, “didn't believe in babysitters,” she joked in an interview. The extensive time she therefore spent in hospitals throughout her childhood showed Khan the life-changing impact of innovative medicines and set her on the path to the C-suites of Johnson & Johnson and Recursion Pharmaceuticals. Learn more
AI maps how a new antibiotic targets gut bacteria
“This discovery speaks to a central challenge in antibiotic development,” says Jon Stokes, senior author of a new paper on the work, assistant professor of biochemistry and biomedical sciences at McMaster, and research affiliate at MIT’s Abdul Latif Jameel Clinic for Machine Learning in Health. “The problem isn’t finding molecules that kill bacteria in a dish — we’ve been able to do that for a long time. A major hurdle is figuring out what those molecules actually do inside bacteria. Without that detailed understanding, you can’t develop these early-stage antibiotics into safe and effective therapies for patients.” Learn more
MIT researchers develop AI tool to improve flu vaccine strain selection
Every year, global health experts are faced with a high-stakes decision: Which influenza strains should go into the next seasonal vaccine? The choice must be made months in advance, long before flu season even begins, and it can often feel like a race against the clock. If the selected strains match those that circulate, the vaccine will likely be highly effective. But if the prediction is off, protection can drop significantly, leading to (potentially preventable) illness and strain on health care systems. Learn more
TIME100 AI 2025
Regina Barzilay is in the business of patient future-telling. That is, using machine learning AI models to predict disease—including when and how it will strike, along with how it may behave. Barzilay began pursuing this after being diagnosed with breast cancer in 2014. As a patient, she experienced the frustrating uncertainty surrounding individual prognoses. Her questions about treatments were often answered in reference to what happened to the participants of clinical trials, but she felt those answers gave her little information about her individual situation.As an AI researcher, she knew how to address that uncertainty. “To me it was quite clear,” she says, “That's what machine learning is about.” A decade later, the AI model she and her team built, named MIRAI, is able to detect a patient’s risk of developing breast cancer within five years. By 2025, MIRAI was validated by over 2 million mammograms in 48 hospitals across 22 countries.
And her future-telling continues. In 2024, Barzilay worked on an AI model that estimates the expected effectiveness of candidate flu vaccines by predicting which versions of the flu virus are likely to spread next season. She’s now working on using the same concept on cancer, in order to predict how patients—particularly with advanced cancers—will react to a specific treatment. “We are constantly running behind the disease,” she says. “The idea here is to be able to predict it.” Learn more
