Thirteen new graduate student fellows will pursue exciting new paths of knowledge and discovery.

MIT researchers develop "FrameDiff," a computational tool that uses generative AI to craft new protein structures, with the aim of accelerating drug development and improving gene therapy.

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.

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.

When coated onto plastic tubing, the catalysts could act on chemicals flowing through, helping to synthesize drugs and other compounds.

A new artificial intelligence technique only proposes candidate molecules that can actually be produced in a lab.

The machine-learning model could help scientists speed the development of new medicines.

A deep learning model rapidly predicts the 3D shapes of drug-like molecules, which could accelerate the process of discovering new medicines.

Paper-based blood test developed by SMART researchers can rapidly determine the presence of SARS-CoV-2 neutralizing antibodies.