Enhancing activity of a specific component of neurons’ “NMDA” receptors normalized protein synthesis, neural activity, and seizure susceptibility in the hippocampus of fragile X lab mice.
Starting with a single frame in a simulation, a new system uses generative AI to emulate the dynamics of molecules, connecting static molecular structures and developing blurry pictures into videos.
Using this model, researchers may be able to identify antibody drugs that can target a variety of infectious diseases.
Junior Katie Spivakovsky describes her path through New Engineering Education Transformation to biomedical research and beyond.
Large multi-ring-containing molecules known as oligocyclotryptamines have never been produced in the lab until now.
Most antibiotics target metabolically active bacteria, but with artificial intelligence, researchers can efficiently screen compounds that are lethal to dormant microbes.
These compounds can kill methicillin-resistant Staphylococcus aureus (MRSA), a bacterium that causes deadly infections.
MIT researchers find that in mice and human cell cultures, lipid nanoparticles can deliver a potential therapy for inflammation in the brain, a prominent symptom in Alzheimer’s.
Brad Pentelute and his lab compel the anthrax delivery system to deliver antibody and peptide variants into cells to treat cancer.
A potential new Alzheimer’s drug represses the harmful inflammatory response of the brain’s immune cells, reducing disease pathology, preserving neurons, and improving cognition in preclinical tests.
A one-week summer program aims to foster a deeper understanding of machine-learning approaches in health among curious young minds.
This AI system only needs a small amount of data to predict molecular properties, which could speed up drug discovery and material development.
By applying a language model to protein-drug interactions, researchers can quickly screen large libraries of potential drug compounds.
The machine-learning algorithm identified a compound that kills Acinetobacter baumannii, a bacterium that lurks in many hospital settings.
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.