New model predicts a chemical reaction’s point of no return
Chemists could use this quick computational method to design more efficient reactions that yield useful compounds, from fuels to pharmaceuticals.
Chemists could use this quick computational method to design more efficient reactions that yield useful compounds, from fuels to pharmaceuticals.
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.