MIT-Takeda Program wraps up with 16 publications, a patent, and nearly two dozen projects completed
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 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.
Twelve finalists participated in initiative and 2023-24 MIT-Royalty Pharma Prize Competition, designed to support female biotech pioneers.
New research addresses a gap in understanding how ketamine’s impact on individual neurons leads to pervasive and profound changes in brain network function.
Stimulating gamma brain waves may protect cancer patients from memory impairment and other cognitive effects of chemotherapy.
Core-shell structures made of hydrogel could enable more efficient uptake in the body.
Thirteen new graduate student fellows will pursue exciting new paths of knowledge and discovery.
A biotech entrepreneur, Koehler will help faculty and students launch startups and bring new products to market through the MIT Deshpande Center for Technological Innovation.
A pilot-scale system, enabled by an $82 million award from the FDA, aims to accelerate the development and production of mRNA technologies.
“FrameDiff” is 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.
With full genetic control and visibility into neural activity and behavior, MIT scientists map out chemical’s role in behavior.
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.