MIT’s DIFFDOCK Boosts the Molecular Docking Top-1 Success Rate from 23% to 38% | Synced
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’...
Source: Synced | AI Technology & Industry Review
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.