More recently, generative AI has shown potential in helping chemists and biologists explore static molecules, like proteins and DNA. Models like AlphaFold can predict molecular structures to accelerate drug discovery, and the MIT-assisted 'RFdiffusion,' for example, can help design new proteins. One challenge, though, is that molecules are constantly moving and jiggling, which is important to model when constructing new proteins and drugs. Simulating these motions on a computer using physics ' a technique known as molecular dynamics ' can be very expensive, requiring billions of time steps on supercomputers.As a step toward simulating these behaviors more efficiently, MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) and Department of Mathematics researchers have developed a generative model that learns from prior data. The team's system, called MDGen, can take a frame of a 3D molecule and simulate what will happen next like a video, connect separate stills, and...
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