  Molecular docking, given a ligand molecule and a ligand binding site (called
``pocket'') on a protein, predicting the binding mode of the protein-ligand
complex, is a widely used technique in drug design. Many deep learning models
have been developed for molecular docking, while most existing deep learning
models perform docking on the whole protein, rather than on a given pocket as
the traditional molecular docking approaches, which does not match common
needs. What's more, they claim to perform better than traditional molecular
docking, but the approach of comparison is not fair, since traditional methods
are not designed for docking on the whole protein without a given pocket. In
this paper, we design a series of experiments to examine the actual performance
of these deep learning models and traditional methods. For a fair comparison,
we decompose the docking on the whole protein into two steps, pocket searching
and docking on a given pocket, and build pipelines to evaluate traditional
methods and deep learning methods respectively. We find that deep learning
models are actually good at pocket searching, but traditional methods are
better than deep learning models at docking on given pockets. Overall, our work
explicitly reveals some potential problems in current deep learning models for
molecular docking and provides several suggestions for future works.
