  Generating molecules, both in a directed and undirected fashion, is a huge
part of the drug discovery pipeline. Genetic algorithms (GAs) generate
molecules by randomly modifying known molecules. In this paper we show that GAs
are very strong algorithms for such tasks, outperforming many complicated
machine learning methods: a result which many researchers may find surprising.
We therefore propose insisting during peer review that new algorithms must have
some clear advantage over GAs, which we call the GA criterion. Ultimately our
work suggests that a lot of research in molecule generation should be
re-assessed.
