Genetic algorithms are strong baselines for molecule generation
This work suggests reassessing research in molecule generation, highlighting a potential oversight in the field.
The paper tackles the problem of molecule generation in drug discovery by demonstrating that genetic algorithms (GAs) outperform many complex machine learning methods, proposing a 'GA criterion' for peer review to require new algorithms to have clear advantages over GAs.
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.