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.
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.
Using biological, chemical, and engineering tools, she has developed strategies to attack molecules once thought to be “undruggable.”
A machine-learning method finds patterns of health decline in ALS, informing future clinical trial designs and mechanism discovery. The technique also extends to Alzheimer’s and Parkinson’s.
A geometric deep-learning model is faster and more accurate than state-of-the-art computational models, reducing the chances and costs of drug trial failures.
The machine-learning model could help scientists speed the development of new medicines.
Neural network identifies synergistic drug blends for treating viruses like SARS-CoV-2.
Researchers find three immunotherapy drugs given together can eliminate pancreatic tumors in mice.
How 3D-printed models of neuronal axons could accelerate development of new therapies to treat neurodegenerative disorders.