MIT’s DIFFDOCK Boosts the Molecular Docking Top-1 Success Rate from 23% to 38%

MIT Researchers propose DIFFDOCK, a diffusion generative model that significantly improves the molecular docking top-1 prediction success rate, from state-of-the-art traditional docking approaches’ 23 percent to 38 percent.

ai artificial intelligence deep-neural-networks machine learning machine learning & data science mit ml molecular docking rate research success technology top