With generative AI, MIT chemists quickly calculate 3D genomic structures
A new approach, which takes minutes rather than days, predicts how a specific DNA sequence will arrange itself in the cell nucleus.
A new approach, which takes minutes rather than days, predicts how a specific DNA sequence will arrange itself in the cell nucleus.
Using this model, researchers may be able to identify antibody drugs that can target a variety of infectious diseases.
By analyzing X-ray crystallography data, the model could help researchers develop new materials for many applications, including batteries and magnets.
MIT researchers plan to search for proteins that could be used to measure electrical activity in the brain.
Using a machine-learning algorithm, researchers can predict interactions that could interfere with a drug’s effectiveness.
Dermatologists and general practitioners are somewhat less accurate in diagnosing disease in darker skin, a new study finds. Used correctly, AI may be able to help.
A new study finds that language regions in the left hemisphere light up when reading uncommon sentences, while straightforward sentences elicit little response.
These compounds can kill methicillin-resistant Staphylococcus aureus (MRSA), a bacterium that causes deadly infections.
Using generative AI, MIT chemists created a model that can predict the structures formed when a chemical reaction reaches its point of no return.
Study shows computational models trained to perform auditory tasks display an internal organization similar to that of the human auditory cortex.