Designing compounds with desired properties is a key
element of
the drug discovery process. However, measuring progress in the field
has been challenging due to the lack of realistic retrospective benchmarks,
and the large cost of prospective validation. To close this gap, we
propose a benchmark based on docking, a widely used computational
method for assessing molecule binding to a protein. Concretely, the
goal is to generate drug-like molecules that are scored highly by
SMINA, a popular docking software. We observe that various graph-based
generative models fail to propose molecules with a high docking score
when trained using a realistically sized training set. This suggests
a limitation of the current incarnation of models forde novodrug design. Finally, we also include simpler tasks in the benchmark
based on a simpler scoring function. We release the benchmark as an
easy to use package available athttps://github.com/cieplinski-tobiasz/smina-docking-benchmark. We hope that our benchmark will serve as a stepping stone toward
the goal of automatically generating promising drug candidates.