BoltzGen generates protein binders for any biological target from scratch, expanding AI’s reach from understanding biology toward engineering it.
Professor Caroline Uhler discusses her work at the Schmidt Center, thorny problems in math, and the ongoing quest to understand some of the most complex interactions in biology.
A new approach can reveal the features AI models use to predict proteins that might make good drug or vaccine targets.
Trained with a joint understanding of protein and cell behavior, the model could help with diagnosing disease and developing new drugs.
The programmable proteins are compact, modular, and can be directed to modify DNA in human cells.
FragFold, developed by MIT Biology researchers, is a computational method with potential for impact on biological research and therapeutic applications.
Whitehead Institute and CSAIL researchers created a machine-learning model to predict and generate protein localization, with implications for understanding and remedying disease.
MIT researchers plan to search for proteins that could be used to measure electrical activity in the brain.
MIT researchers develop “FrameDiff,” a computational tool that uses generative AI to craft new protein structures, with the aim of accelerating drug development and improving gene therapy.
By applying a language model to protein-drug interactions, researchers can quickly screen large libraries of potential drug compounds.
