When Regina Barzilay was diagnosed with breast cancer in 2014, it upended her life and shifted the direction of her research. Already an accomplished computer scientist specializing in natural language processing, her experience as a patient shed light on the possibility of new applications for machine learning and revealed a stark disconnect between technology’s promise and its implementation in health care. “It was upsetting to see that all these great technologies are not translated into patient care,” she recalls. “I wanted to change it.” After going through her own treatment, Barzilay’s work took on an urgent new focus: could the very technologies she used in her research predict who might be at risk for breast cancer? Learn more
Researchers from the Massachusetts Institute of Technology (MIT) Jameel Clinic for Machine Learning in Health have announced the open-source release of Boltz-2, which now predicts molecular binding affinity at newfound speed and accuracy to democratize commercial drug discovery. The model is available under the highly permissive MIT license, which allows commercial drug developers to use the model internally and apply their own proprietary data. Learn more
The 2024 Nobel Prize in Chemistry was awarded in part to Deepmind’s Demis Hassabis and John Jumper for the development of AlphaFold–an AI model that predicts the structure of proteins, the complex chemicals essential to making our bodies work. Since its inception, this model and others like it have been put to use in laboratories around the world, enabling new biological discoveries.
Now a team from MIT and pharmaceutical company Recursion, with support from Cancer Grand Challenges, have developed a tool that takes these principles further–and may help researchers find new medicines more quickly. Called Boltz-2, this open-source generative AI model can not only predict the structure of proteins, it can also predict its binding affinity–that is, how well a potential drug is able to interact with that protein. This is crucial in the early stages of developing a new medicine. Learn more
During my seven years as president and CEO of Susan G. Komen, I’ve witnessed the impact of innovation in our mission to end breast cancer for good. Over the past four decades, we’ve seen a 44% reduction in breast cancer mortality, thanks to early detection and better treatments. AI is now accelerating this progress with its capacity to analyze vast datasets, discover new patterns and enhance diagnostic accuracy.
Take, for example, the groundbreaking work of Komen scholar Dr. Regina Barzilay. Using her own mammograms in her research at MIT, Dr. Barzilay demonstrated how AI could have detected her breast cancer much earlier, potentially improving her prognosis. Studies show that incorporating AI into mammogram analysis boosts cancer detection rates by 20%, without increasing false positives. This is a significant leap forward, as early detection is key to a better chance at positive outcomes and survival. Learn more
