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.

翻译：分子生成（包括定向与非定向生成）是药物发现流程中的关键环节。遗传算法通过随机修改已知分子来生成新分子。本文证明，遗传算法在此类任务中表现极为出色，其性能超越了许多复杂的机器学习方法——这一结果或许会让许多研究者感到意外。因此，我们建议在同行评审中坚持要求新算法必须具有相对于遗传算法的明确优势，我们将此称为遗传算法准则。最终，本研究结果表明，许多关于分子生成的研究工作需要进行重新评估。