Accelerating particle size distribution estimation
MIT researchers speed up a novel AI-based estimator for medication manufacturing by 60 times.
MIT researchers speed up a novel AI-based estimator for medication manufacturing by 60 times.
The program focused on AI in health care, drawing on Takeda’s R&D experience in drug development and MIT’s deep expertise in AI.
The SPARROW algorithm automatically identifies the best molecules to test as potential new medicines, given the vast number of factors affecting each choice.
Co-hosted by the McGovern Institute, MIT Open Learning, and others, the symposium stressed emerging technologies in advancing understanding of mental health and neurological conditions.
Thirteen new graduate student fellows will pursue exciting new paths of knowledge and discovery.
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.
MIT-Novo Nordisk Artificial Intelligence Postdoctoral Fellows Program will support up to 10 postdocs annually over five years.
A collaborative research team from the MIT-Takeda Program combined physics and machine learning to characterize rough particle surfaces in pharmaceutical pills and powders.
MIT researchers built DiffDock, a model that may one day be able to find new drugs faster than traditional methods and reduce the potential for adverse side effects.
The program leverages MIT’s research expertise and Takeda’s industrial know-how for research in artificial intelligence and medicine.