  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 popular 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 popular
graph-based generative models fail to generate molecules with a high docking
score when trained using a realistically sized training set. This suggests a
limitation of the current incarnation of models for de novo drug design.
Finally, we propose a simplified version of the benchmark based on a simpler
scoring function, and show that the tested models are able to partially solve
it. We release the benchmark as an easy to use package available at
https://github.com/cieplinski-tobiasz/smina-docking-benchmark. We hope that our
benchmark will serve as a stepping stone towards the goal of automatically
generating promising drug candidates.

Biomolecules;Machine Learning;Machine Learning