DiffDock is a diffusion-based ML model for the docking of a protein to a small molecule. DiffDock reports a confidence score (-inf, inf) where a larger number is better, and scores below -1.5 can be considered low confidence. Note the confidence score does not predict the affinity, aka strength of the biochemical attraction, of the complex.
Molecular docking predicts how two molecules fit together in 3D and a staple method in computer-aided drug design. For example, a small molecule drug with its target enzyme, an antibody drug with its target antigen, or a peptide hormone with its natural receptor. Docking is also called cofolding when folding (i.e. protein structure prediction) is done at the same time. By studying how molecules dock into a complex, scientists can learn how a drug-target interaction is working as part of the drug discovery process or better understand how our bodies work. While experimental results from cryo-EM or crystallography are the gold standard, docking offers a much faster and economical computational solution.