At the core of problem-solving
Stuart Levine ’97, director of MIT’s BioMicro Center, keeps departmental researchers at the forefront of systems biology.
School of Science
An ancient RNA-guided system could simplify delivery of gene editing therapies
The programmable proteins are compact, modular, and can be directed to modify DNA in human cells.
AI system predicts protein fragments that can bind to or inhibit a target
FragFold, developed by MIT Biology researchers, is a computational method with potential for impact on biological research and therapeutic applications.
AI model deciphers the code in proteins that tells them where to go
Whitehead Institute and CSAIL researchers created a machine-learning model to predict and generate protein localization, with implications for understanding and remedying disease.
Introducing the MIT Generative AI Impact Consortium
The consortium will bring researchers and industry together to focus on impact.
With generative AI, MIT chemists quickly calculate 3D genomic structures
A new approach, which takes minutes rather than days, predicts how a specific DNA sequence will arrange itself in the cell nucleus.
For healthy hearing, timing matters
Machine-learning models let neuroscientists study the impact of auditory processing on real-world hearing.
Teaching AI to communicate sounds like humans do
Inspired by the mechanics of the human vocal tract, a new AI model can produce and understand vocal imitations of everyday sounds. The method could help build new sonic interfaces for entertainment and education.
A new computational model can predict antibody structures more accurately
Using this model, researchers may be able to identify antibody drugs that can target a variety of infectious diseases.
MIT welcomes Frida Polli as its next visiting innovation scholar
The neuroscientist turned entrepreneur will be hosted by the MIT Schwarzman College of Computing and focus on advancing the intersection of behavioral science and AI across MIT.